Title:	Analysis of Diffusion and Contagion Processes on Networks
Version:	1.23.0
Description:	Empirical statistical analysis, visualization and simulation of diffusion and contagion processes on networks. The package implements algorithms for calculating network diffusion statistics such as transmission rate, hazard rates, exposure models, network threshold levels, infectiousness (contagion), and susceptibility. The package is inspired by work published in Valente, et al., (2015) <doi:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN: 9781881303213>, Myers (2000) <doi:10.1086/303110>, Iyengar and others (2011) <doi:10.1287/mksc.1100.0566>, Burt (1987) <doi:10.1086/228667>; among others.
Depends:	R (≥ 3.1.1)
License:	MIT + file LICENSE
LazyData:	true
Imports:	Rcpp (≥ 0.12.1), sna, network, networkDynamic, Matrix, MASS, MatchIt, SparseM, methods, grDevices, graphics, stats, utils, boot, igraph, viridisLite
Suggests:	covr, testthat, knitr, rmarkdown, ape, RSiena, survival
VignetteBuilder:	knitr
LinkingTo:	Rcpp, RcppArmadillo
RoxygenNote:	7.3.2
Encoding:	UTF-8
URL:	https://github.com/USCCANA/netdiffuseR, https://USCCANA.github.io/netdiffuseR/
BugReports:	https://github.com/USCCANA/netdiffuseR/issues
Classification/MSC:	90C35, 90B18, 91D30
Collate:	'RcppExports.R' 'imports.r' 'graph_data.r' 'adjmat.r' 'bass.r' 'bootnet.r' 'citer_environment.R' 'data.r' 'diffnet-c.R' 'diffnet-class.r' 'diffnet-indexing.r' 'diffnet-methods.r' 'egonets.R' 'formula.r' 'igraph.r' 'infect_suscept.r' 'mentor.r' 'misc.r' 'moran.r' 'netmatch.r' 'network.r' 'options.R' 'package-doc.r' 'plot_diffnet2.r' 'rewire.r' 'random_graph.R' 'rdiffnet.r' 'read_write_foreign.r' 'select_egoalter.R' 'spatial.R' 'stats.R' 'struct_equiv.R' 'struct_test.R' 'survey_to_diffnet.R'
NeedsCompilation:	yes
Packaged:	2025-06-12 15:52:49 UTC; runner
Author:	George Vega Yon [aut, cre] (what: Rewrite functions with Rcpp, plus new features), Thomas Valente [aut, cph] (what: R original code), Anibal Olivera Morales [aut, ctb] (what: Multi-diffusion version), Stephanie Dyal [ctb] (Package's first version), Timothy Hayes [ctb] (Package's first version)
Maintainer:	George Vega Yon <g.vegayon@gmail.com>
Repository:	CRAN
Date/Publication:	2025-06-12 17:00:01 UTC

netdiffuseR

Description

Statistical analysis, visualization and simulation of diffusion and contagion processes on networks. The package implements algorithms for calculating stats such as innovation threshold levels, infectiousness (contagion) and susceptibility, and hazard rates as presented in Burt (1987), Valente (1995), and Myers (2000) (among others).

You can access to the project website at https://github.com/USCCANA/netdiffuseR

Details

Analysis of Diffusion and Contagion Processes on Networks

Acknowledgements

netdiffuseR was created with the support of grant R01 CA157577 from the National Cancer Institute/National Institutes of Health.

Workshops and Tutorials

Online you can find several learning resources, particularly, at the netdiffuseR workshop website: <https://github.com/USCCANA/netdiffuser-workshop>.

Author(s)

George G. Vega Yon & Thomas W. Valente

Matrix multiplication

Description

Matrix multiplication methods, including diffnet objects. This function creates a generic method for %*% allowing for multiplying diffnet objects.

Usage

x %*% y

## Default S3 method:
x %*% y

## S3 method for class 'diffnet'
x %*% y

Arguments

Details

This function can be usefult to generate alternative graphs, for example, users could compute the n-steps graph by doing net %*% net (see examples).

Value

In the case of diffnet objects performs matrix multiplication via mapply using x$graph and y$graph as arguments, returnling a diffnet. Otherwise returns the default according to %*%.

See Also

Other diffnet methods: as.array.diffnet(), c.diffnet(), diffnet-arithmetic, diffnet-class, diffnet_index, plot.diffnet(), summary.diffnet()

Examples

# Finding the Simmelian Ties network ----------------------------------------

# Random diffnet graph
set.seed(773)
net <- rdiffnet(100, 4, seed.graph='small-world', rgraph.args=list(k=8))
netsim <- net

# According to Dekker (2006), Simmelian ties can be computed as follows
netsim <- net * t(net) # Keeping mutal
netsim <- netsim * (netsim %*% netsim)

# Checking out differences (netsim should have less)
nlinks(net)
nlinks(netsim)

mapply(`-`, nlinks(net), nlinks(netsim))

Approximate Geodesic Distances

Description

Computes approximate geodesic distance matrix using graph powers and keeping the amount of memory used low.

Usage

approx_geodesic(graph, n = 6L, warn = FALSE)

approx_geodist(graph, n = 6L, warn = FALSE)

Arguments

Details

While both igraph and sna offer very good and computationally efficient routines for computing geodesic distances, both functions return dense matrices, i.e. not sparse, which can be troublesome. Furthermore, from the perspective of social network analysis, path lengths of more than 6 steps, for example, may not be meaningful, or at least, relevant for the researcher. In such cases, approx_geodesic serves as a solution to this problem, computing geodesics up to the number of steps, n, desired, hence, if n = 6, once the algorithm finds all paths of 6 or less steps it will stop, returning a sparse matrix with zeros for those pairs of vertices for which it was not able to find a path with less than n steps.

Depending on the graph size and density, approx_geodesic's performance can be compared to that of sna::geodist. Although, as n increases, geodist becomes a better alternative.

The algorithm was implemented using power graphs. At each itereation i the power graph of order i is computed, and its values are compared to the current values of the geodesic matrix (which is initialized in zero).

1. Initialize the output ans(n, n)

2. For i=1 to i < n do

   1. Iterate through the edges of G^i, if ans has a zero value in the corresponding row+column, replace it with i

   2. next

3. Replace all diagonal elements with a zero and return.

This implementation can be more memory efficient that the aforementioned ones, but at the same time it can be significant slower.

approx_geodist is just an allias for approx_geodesic.

Value

A sparse matrix of class dgCMatrix of size nnodes(graph)^2 with geodesic distances up to n.

Examples

# A very simple example -----------------------------------------------------
g <- ring_lattice(10, 3)
approx_geodesic(g, 6)
sna::geodist(as.matrix(g))[[2]]
igraph::distances(
  igraph::graph_from_adjacency_matrix(g, mode = "directed"),
  mode = "out"
)

Coerce a diffnet graph into an array

Description

Coerce a diffnet graph into an array

Usage

## S3 method for class 'diffnet'
as.array(x, ...)

Arguments

Details

The function takes the list of sparse matrices stored in x and creates an array with them. Attributes and other elements from the diffnet object are dropped.

dimnames are obtained from the metadata of the diffnet object.

Value

A three-dimensional array of T matrices of size n\times n.

See Also

diffnet.

Other diffnet methods: %*%(), c.diffnet(), diffnet-arithmetic, diffnet-class, diffnet_index, plot.diffnet(), summary.diffnet()

Examples

# Creating a random diffnet object
set.seed(84117)
mydiffnet <- rdiffnet(30, 5)

# Coercing it into an array
as.array(mydiffnet)

Coerce a matrix-like objects to dgCMatrix (sparse matrix)

Description

This helper function allows easy coercion to sparse matrix objects from the Matrix package, dgCMatrix.

Usage

as_dgCMatrix(x, make.dimnames = TRUE, ...)

as.dgCMatrix(x, make.dimnames = TRUE, ...)

as_spmat(x, make.dimnames = TRUE, ...)

## Default S3 method:
as_dgCMatrix(x, make.dimnames = TRUE, ...)

## S3 method for class 'diffnet'
as_dgCMatrix(x, make.dimnames = TRUE, ...)

## S3 method for class 'array'
as_dgCMatrix(x, make.dimnames = TRUE, ...)

## S3 method for class 'igraph'
as_dgCMatrix(x, make.dimnames = TRUE, ...)

## S3 method for class 'network'
as_dgCMatrix(x, make.dimnames = TRUE, ...)

## S3 method for class 'list'
as_dgCMatrix(x, make.dimnames = TRUE, ...)

Arguments

Details

In the case of the igraph and network methods, ... is passed to as_adj and as.matrix.network respectively.

Value

Either a list with dgCMatrix objects or a dgCMatrix object.

Examples

set.seed(1231)
x <- rgraph_er(10)

# From matrix object
as_dgCMatrix(as.matrix(x))

# From a network object
as_dgCMatrix(network::as.network(as.matrix(x)))

# From igraph object
as_dgCMatrix(igraph::graph_from_adjacency_matrix(x))

# From array
myarray <- array(dim=c(10,10,2))
myarray[,,1] <- as.matrix(x)
myarray[,,2] <- as.matrix(x)

myarray
as_dgCMatrix(myarray)

# From a diffnet object
ans <- as_dgCMatrix(medInnovationsDiffNet)
str(ans)

Bass Model

Description

Fits the Bass Diffusion model. In particular, fits an observed curve of proportions of adopters to F(t), the proportion of adopters at time t, finding the corresponding coefficients p, Innovation rate, and q, imitation rate.

Usage

fitbass(dat, ...)

## S3 method for class 'diffnet'
fitbass(dat, ...)

## Default S3 method:
fitbass(dat, ...)

## S3 method for class 'diffnet_bass'
plot(
  x,
  y = 1:length(x$m$lhs()),
  add = FALSE,
  pch = c(21, 24),
  main = "Bass Diffusion Model",
  ylab = "Proportion of adopters",
  xlab = "Time",
  type = c("b", "b"),
  lty = c(2, 1),
  col = c("black", "black"),
  bg = c("lightblue", "gray"),
  include.legend = TRUE,
  ...
)

bass_F(Time, p, q)

bass_dF(p, q, Time)

bass_f(Time, p, q)

Arguments

Details

The function fits the bass model with parameters [p, q] for values t = 1, 2, \dots, T, in particular, it fits the following function:

F(t) = \frac{1 - \exp{-(p+q)t}}{1 + \frac{q}{p}\exp{-(p+q)t}}

Which is implemented in the bass_F function. The proportion of adopters at time t, f(t) is:

f(t) = \left\{\begin{array}{ll} F(t), & t = 1 \\ F(t) - F(t-1), & t > 1 \end{array}\right.

and it's implemented in the bass_f function.

For testing purposes only, the gradient of F with respect to p and q is implemented in bass_dF.

The estimation is done using nls.

Value

An object of class nls and diffnet_bass. For more details, see nls in the stats package.

Author(s)

George G. Vega Yon

References

Bass's Basement Institute Institute. The Bass Model. (2010). Available at: https://web.archive.org/web/20220331222618/http://www.bassbasement.org/BassModel/. (accessed live for the last time on March 29th, 2017.)

See Also

Other statistics: classify_adopters(), cumulative_adopt_count(), dgr(), ego_variance(), exposure(), hazard_rate(), infection(), moran(), struct_equiv(), threshold(), vertex_covariate_dist()

Examples

# Fitting the model for the Brazilian Farmers Data --------------------------
data(brfarmersDiffNet)
ans <- fitbass(brfarmersDiffNet)

# All the methods that work for the -nls- object work here
ans
summary(ans)
coef(ans)
vcov(ans)

# And the plot method returns both, fitted and observed curve
plot(ans)

Network Bootstrapping

Description

Implements the bootstrapping method described in Snijders and Borgatti (1999). This function is essentially a wrapper of boot.

Usage

resample_graph(graph, self = NULL, useR = FALSE, ...)

bootnet(graph, statistic, R, resample.args = list(self = FALSE), ...)

## S3 method for class 'diffnet_bootnet'
c(..., recursive = FALSE)

## S3 method for class 'diffnet_bootnet'
print(x, ...)

## S3 method for class 'diffnet_bootnet'
hist(
  x,
  main = "Empirical Distribution of Statistic",
  xlab = expression(Values ~ of ~ t),
  breaks = 20,
  annotated = TRUE,
  b0 = expression(atop(plain("") %up% plain("")), t[0]),
  b = expression(atop(plain("") %up% plain("")), t[]),
  ask = TRUE,
  ...
)

## S3 method for class 'diffnet_bootnet'
plot(x, y, ...)

Arguments

Details

Just like the boot function of the boot package, the statistic that is passed must have as arguments the original data (the graph in this case), and a vector of indicides. In each repetition, the graph that is passed is a resampled version generated as described in Snijders and Borgatti (1999).

When self = FALSE, for pairs of individuals that haven been drawn more than once the algorithm, in particular, resample_graph, takes care of filling these pseudo autolinks that are not in the diagonal of the network. By default it is assumed that these pseudo-autolinks depend on whether the original graph had any, hence, if the diagonal has any non-zero value the algorithm assumes that self = TRUE, skiping the 'filling algorithm'. It is important to notice that, in order to preserve the density of the original network, when assigning an edge value to a pair of the form (i,i) (pseudo-autolinks), such is done with probabilty proportional to the density of the network, in other words, before choosing from the existing list of edge values, the algorithm decides whether to set a zero value first.

The vector of indices that is passed to statistic, an integer vector with range 1 to n, corresponds to the drawn sample of nodes, so the user can, for example, use it to get a subset of a data.frame that will be used with the graph.

The 'plot.diffnet_bootnet' method is a wrapper for the 'hist' method.

Value

A list of class diffnet_bootnet containing the following:

References

Snijders, T. A. B., & Borgatti, S. P. (1999). Non-Parametric Standard Errors and Tests for Network Statistics. Connections, 22(2), 1–10. Retrieved from https://www.stats.ox.ac.uk/~snijders/Snijders_Borgatti.pdf

See Also

Other Functions for inference: moran(), struct_test()

Examples

# Computing edgecount -------------------------------------------------------
set.seed(13)
g <- rgraph_ba(t=99)

ans <- bootnet(g, function(w, ...) length(w@x), R=100)
ans

# Generating

Brazilian Farmers

Description

From Valente (1995) “In the mid-1960s, Rogers and others conducted an ambitious ‘three country study’ to determine influences on adoption of farm practices in Nigeria, India and Brazil. [...] Only in Brazil, and only for hybrid corn, did adoption of the innovation reach more than a small proportion of the farmers.”

Usage

brfarmers

Format

A data frame with 692 rows and 148 columns:

village

village number

idold

respondent id

age

respondent's age

liveout

Lived outside of community

visits

# of visits to large city

contact

# of contacts with relatives

coop

membership in coop

orgs

membership in organizations

patry

Patriarchalism score

liter

Literate

news1

# of newspapers or mags pr mon

subs

subscribe to news

radio1

Own radio

radio2

Frequency radio listening

radio3

program preference

tv

frequency Tv viewing

movie

freq movie attendance

letter

freq letter writing

source

total # of sources used for ag

practA

Ever used practice A

practB

Ever used practice B

practC

Ever used practice C

practD

Ever used practice D

practE

Ever used practice E

practF

Ever used practice F

practG

Ever used practice G

practH

Ever used practice H

practI

Ever used practice I

practJ

Ever used practice J

practK

Ever used practice K

practL

Ever used practice L

yrA

A year of adoption

yrB

B year of adoption

yrC

C year of adoption

yrD

D year of adoption

yrE

E year of adoption

yrF

F year of adoption

yrG

G year of adoption

yrH

H year of adoption

yrI

I year of adoption

yrJ

J year of adoption

yrK

K year of adoption

yrL

L year of adoption

curA

A Current use

curB

B Current use

curC

C Current use

curD

D Current use

curE

E Current use

curF

F Current use

curG

G Current use

curH

H Current use

curI

I Current use

curJ

J Current use

curK

K Current use

curL

L Current use

srce1

Source of aware in A

timeA

Years ago 1st aware

src2

Source of more info on A

src3

Most influential source

use

use during trial stage

total

total # of practices adopted

futatt

Future attitude

achiev

Achievement Score

attcred

Attitude toward credit

littest

Score on functional literacy t

acarcomm

Communication with ACAR repres

econk

Economic knowledge

caact

recognize any change agent act

hfequip

# of home & farm equips owned

politk

political knowledge score

income

income

land1

total land area in pasture

land2

total land area planted

cows

# of cows giving milk

land3

total land owned

respf

respondent named as friend

respa

respondent named as ag adv

resppa

respondent named for practic A

resppb

respondent named for practic B

resppc

respondent named for practic C

poly

polymorphic OL for 3 practices

respl

respondent named for loan

resppi

resp named for price info

repsccp

resp named for coop comm proj

counter

counterfactuality score

opinion

opinionness score

school

years of schooling by resp

pk1

political know 1

pk2

political know 2

pk3

political know 3

pk4

political know 4

pk5

political know 5

innovtim

innovativeness time

adoptpct

adoption percent

discon

# of practices discontinued

mmcred

Mass media credibility

trust

Trust

stusincn

Status inconsistency

nach

N achievement motivation

attcred2

Attitude toward credit

risk

Risk taking

socpart

Social participate

patriarc

patriarchy

crdit2

attit to credit for product

visicit

visitin cities

nondep

non-dependence on farming

oltotal

OL total 7 items t-score

innov

overall innovativeness score

icosmo

cosmo index

immexp

mass media exposure index

iempath

empathy index

iach5

achievement motivation index 5

iach7

achievement motivation index 7

ipk

political knowledge index

immc

mass media credibililty index

iol

OL index

yr

Actual Year of Adoption

fs

— MISSING INFO —

ado

Time of Adoption

tri

Triangular values used as appro

hlperc

high low percent of diffusion

hlperc1

— MISSING INFO —

new

new or old villages

card1

card number

sour1

Source: radio

sour2

Source: TV

sour3

Source: Newpaper

sour4

Source: Magazine

sour5

Source: ACAR Bulletin

sour6

Source: Agronomist

sour7

Source: Neighbor

sourc6

— MISSING INFO —

adopt

— MISSING INFO —

net31

nomination friend 1

net32

nomination friend 2

net33

nomination friend 3

net21

nomination influential 1

net22

nomination influential 2

net23

nomination influential 3

net11

nomination practice A

net12

nomination practice B

net13

nomination practice C

net41

nomination coop comm proj

id

— MISSING INFO —

commun

Number of community

toa

Time of Adoption

test

— MISSING INFO —

study

Number of study in Valente (1995)

Details

The dataset has 692 respondents (farmers) from 11 communities. Collected during 1966, it spans 20 years of farming pracitices.

Source

The Brazilian Farmers data were collected as part of a USAID-funded study of farming practicing in the three countries, India, Nigeria, and Brazil. There was only one wave of data that contained survey questions regarding social networks, and only in Brazil did diffusion of the studied farming innovations reach an appreciable saturation level- that was for hybrid seed corn. The data were stored along with hundreds of other datasets by the University of Wisconsin library and I, Tom Valente, paid a fee to have the disks mailed to me in the early 1990s.

References

Rogers, E. M., Ascroft, J. R., & Röling, N. (1970). Diffusion of Innovation in Brazil, Nigeria, and India. Unpublished Report. Michigan State University, East Lansing.

Valente, T. W. (1995). Network models of the diffusion of innovations (2nd ed.). Cresskill N.J.: Hampton Press.

See Also

Other diffusion datasets: brfarmersDiffNet, diffusion-data, fakeDynEdgelist, fakeEdgelist, fakesurvey, fakesurveyDyn, kfamily, kfamilyDiffNet, medInnovations, medInnovationsDiffNet

diffnet version of the Brazilian Farmers data

Description

A directed dynamic graph with 692 vertices and 21 time periods. The attributes in the graph are static and described in brfarmers.

Format

A diffnet class object.

See Also

Other diffusion datasets: brfarmers, diffusion-data, fakeDynEdgelist, fakeEdgelist, fakesurvey, fakesurveyDyn, kfamily, kfamilyDiffNet, medInnovations, medInnovationsDiffNet

Combine diffnet objects

Description

Combining diffnet objects that share time periods and attributes names, but vertices ids (only valid for diffnet objects that have an empty intersection between vertices ids).

Usage

## S3 method for class 'diffnet'
c(..., recursive = FALSE)

Arguments

Details

The diffnet objects in ... must fulfill the following conditions:

1. Have the same time range,

2. have the same vertex attributes, and

3. have an empty intersection of vertices ids,

The meta data regarding undirected, value, and multiple are set to TRUE if any of the concatenating diffnet objects has that meta equal to TRUE.

The resulting diffnet object's columns in the vertex attributes ordering (both dynamic and static) will coincide with the first diffnet's ordering.

Value

A new diffnet object with as many vertices as the sum of each concatenated diffnet objects' number of vertices.

See Also

Other diffnet methods: %*%(), as.array.diffnet(), diffnet-arithmetic, diffnet-class, diffnet_index, plot.diffnet(), summary.diffnet()

Examples

# Calculate structural equivalence exposure by city -------------------------
data(medInnovationsDiffNet)

# Subsetting diffnets
city1 <- medInnovationsDiffNet[medInnovationsDiffNet[["city"]] == 1]
city2 <- medInnovationsDiffNet[medInnovationsDiffNet[["city"]] == 2]
city3 <- medInnovationsDiffNet[medInnovationsDiffNet[["city"]] == 3]
city4 <- medInnovationsDiffNet[medInnovationsDiffNet[["city"]] == 4]

# Computing exposure in each one
city1[["expo_se"]] <- exposure(city1, alt.graph="se", valued=TRUE)
city2[["expo_se"]] <- exposure(city2, alt.graph="se", valued=TRUE)
city3[["expo_se"]] <- exposure(city3, alt.graph="se", valued=TRUE)
city4[["expo_se"]] <- exposure(city4, alt.graph="se", valued=TRUE)

# Concatenating all
diffnet <- c(city1, city2, city3, city4)
diffnet

Classify adopters accordingly to Time of Adoption and Threshold levels.

Description

Adopters are classified as in Valente (1995). In general, this is done depending on the distance in terms of standard deviations from the mean of Time of Adoption and Threshold.

Usage

classify_adopters(...)

classify(...)

## S3 method for class 'diffnet'
classify_adopters(graph, include_censored = FALSE, ...)

## Default S3 method:
classify_adopters(
  graph,
  toa,
  t0 = NULL,
  t1 = NULL,
  expo = NULL,
  include_censored = FALSE,
  ...
)

## S3 method for class 'diffnet_adopters'
ftable(x, as.pcent = TRUE, digits = 2, ...)

## S3 method for class 'diffnet_adopters'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

## S3 method for class 'diffnet_adopters'
plot(x, y = NULL, ftable.args = list(), table.args = list(), ...)

Arguments

Details

Classifies (only) adopters according to time of adoption and threshold as described in Valente (1995). In particular, the categories are defined as follow:

For Time of Adoption, with toa as the vector of times of adoption:

• Early Adopters: toa[i] <= mean(toa) - sd(toa),

• Early Majority: mean(toa) - sd(toa) < toa[i] <= mean(toa) ,

• Late Majority: mean(toa) < toa[i] <= mean(toa) + sd(toa) , and

• Laggards: mean(toa) + sd(toa) < toa[i] .

For Threshold levels, with thr as the vector of threshold levels:

• Very Low Thresh.: thr[i] <= mean(thr) - sd(thr),

• Low Thresh.: mean(thr) - sd(thr) < thr[i] <= mean(thr) ,

• High Thresh.: mean(thr) < thr[i] <= mean(thr) + sd(thr) , and

• Very High. Thresh.: mean(thr) + sd(thr) < thr[i] .

By default threshold levels are not computed for left censored data. These will have a NA value in the thr vector.

The plot method, plot.diffnet_adopters, is a wrapper for the plot.table method. This generates a mosaicplot plot.

Value

A list of class diffnet_adopters with the following elements:

Author(s)

George G. Vega Yon

References

Valente, T. W. (1995). "Network models of the diffusion of innovations" (2nd ed.). Cresskill N.J.: Hampton Press.

See Also

Other statistics: bass, cumulative_adopt_count(), dgr(), ego_variance(), exposure(), hazard_rate(), infection(), moran(), struct_equiv(), threshold(), vertex_covariate_dist()

Examples

# Classifying brfarmers -----------------------------------------------------

x <- brfarmersDiffNet
diffnet.toa(x)[x$toa==max(x$toa, na.rm = TRUE)] <- NA
out <- classify_adopters(x)

# This is one way
round(
with(out, ftable(toa, thr, dnn=c("Time of Adoption", "Threshold")))/
  nnodes(x[!is.na(x$toa)])*100, digits=2)

# This is other
ftable(out)

# Can be coerced into a data.frame, e.g. ------------------------------------
 str(classify(brfarmersDiffNet))
 ans <- cbind(
 as.data.frame(classify(brfarmersDiffNet)), brfarmersDiffNet$toa
 )
 head(ans)

# Creating a mosaic plot with the medical innovations -----------------------
x <- classify(medInnovationsDiffNet)
plot(x)

Analyze an R object to identify the class of graph (if any)

Description

Analyze an R object to identify the class of graph (if any)

Usage

classify_graph(graph)

Arguments

Details

This function analyzes an R object and tries to classify it among the accepted classes in netdiffuseR. If the object fails to fall in one of the types of graphs the function returns with an error indicating what (and when possible, where) the problem lies.

The function was designed to be used with as_diffnet.

Value

Whe the object fits any of the accepted graph formats, a list of attributes including

Otherwise returns with error.

Author(s)

George G. Vega Yon

See Also

as_diffnet, netdiffuseR-graphs

Cummulative count of adopters

Description

For each time period, calculates the number of adopters, the proportion of adopters, and the adoption rate.

Usage

cumulative_adopt_count(obj)

Arguments

Details

The rate of adoption–returned in the 3rd row out the resulting matrix–is calculated as

\frac{q_t - q_{t-1}}{q_{t-1}}

where q_i is the number of adopters in time t. Note that it is only calculated fot t>1.

Value

A 3\times T matrix, where its rows contain the number of adoptes, the proportion of adopters and the rate of adoption respectively, for earch period of time.

Author(s)

George G. Vega Yon & Thomas W. Valente

See Also

Other statistics: bass, classify_adopters(), dgr(), ego_variance(), exposure(), hazard_rate(), infection(), moran(), struct_equiv(), threshold(), vertex_covariate_dist()

Indegree, outdegree and degree of the vertices

Description

Computes the requested degree measure for each node in the graph.

Usage

dgr(
  graph,
  cmode = "degree",
  undirected = getOption("diffnet.undirected", FALSE),
  self = getOption("diffnet.self", FALSE),
  valued = getOption("diffnet.valued", FALSE)
)

## S3 method for class 'diffnet_degSeq'
plot(
  x,
  breaks = min(100L, nrow(x)/5),
  freq = FALSE,
  y = NULL,
  log = "xy",
  hist.args = list(),
  slice = ncol(x),
  xlab = "Degree",
  ylab = "Freq",
  ...
)

Arguments

Value

A numeric matrix of size n\times T. In the case of plot, returns an object of class histogram.

Author(s)

George G. Vega Yon

See Also

Other statistics: bass, classify_adopters(), cumulative_adopt_count(), ego_variance(), exposure(), hazard_rate(), infection(), moran(), struct_equiv(), threshold(), vertex_covariate_dist()

Other visualizations: diffusionMap(), drawColorKey(), grid_distribution(), hazard_rate(), plot_adopters(), plot_diffnet(), plot_diffnet2(), plot_infectsuscep(), plot_threshold(), rescale_vertex_igraph()

Examples

# Comparing degree measurements ---------------------------------------------
# Creating an undirected graph
graph <- rgraph_ba()
graph

data.frame(
   In=dgr(graph, "indegree", undirected = FALSE),
   Out=dgr(graph, "outdegree", undirected = FALSE),
   Degree=dgr(graph, "degree", undirected = FALSE)
 )

# Testing on Korean Family Planning (weighted graph) ------------------------
data(kfamilyDiffNet)
d_unvalued <- dgr(kfamilyDiffNet, valued=FALSE)
d_valued   <- dgr(kfamilyDiffNet, valued=TRUE)

any(d_valued!=d_unvalued)

# Classic Scale-free plot ---------------------------------------------------
set.seed(1122)
g <- rgraph_ba(t=1e3-1)
hist(dgr(g))

# Since by default uses logscale, here we suppress the warnings
# on points been discarded for <=0.
suppressWarnings(plot(dgr(g)))

Creates a square matrix suitable for spatial statistics models.

Description

Creates a square matrix suitable for spatial statistics models.

Usage

diag_expand(...)

## S3 method for class 'list'
diag_expand(graph, self = is_self(graph), valued = is_valued(graph), ...)

## S3 method for class 'diffnet'
diag_expand(graph, self = is_self(graph), valued = is_valued(graph), ...)

## S3 method for class 'matrix'
diag_expand(graph, nper, self = is_self(graph), valued = is_valued(graph), ...)

## S3 method for class 'array'
diag_expand(graph, self = is_self(graph), valued = is_valued(graph), ...)

## S3 method for class 'dgCMatrix'
diag_expand(graph, nper, self = is_self(graph), valued = is_valued(graph), ...)

Arguments

Value

A square matrix of class dgCMatrix of size (nnode(g)*nper)^2

Examples

# Simple example ------------------------------------------------------------
set.seed(23)
g <- rgraph_er(n=10, p=.5, t=2,undirected=TRUE)

# What we've done: A list with 2 bernoulli graphs
g

# Expanding to a 20*20 matrix with structural zeros on the diagonal
# and on cell 'off' adjacency matrix
diag_expand(g)

diffnet Arithmetic and Logical Operators

Description

Addition, subtraction, network power of diffnet and logical operators such as & and | as objects

Usage

## S3 method for class 'diffnet'
x ^ y

graph_power(x, y, valued = getOption("diffnet.valued", FALSE))

## S3 method for class 'diffnet'
y / x

## S3 method for class 'diffnet'
x - y

## S3 method for class 'diffnet'
x * y

## S3 method for class 'diffnet'
x & y

## S3 method for class 'diffnet'
x | y

Arguments

Details

Using binary operators, ease data management process with diffnet.

By default the binary operator ^ assumes that the graph is valued, hence the power is computed using a weighted edges. Otherwise, if more control is needed, the user can use graph_power instead.

Value

A diffnet class object

See Also

Other diffnet methods: %*%(), as.array.diffnet(), c.diffnet(), diffnet-class, diffnet_index, plot.diffnet(), summary.diffnet()

Examples

# Computing two-steps away threshold with the Brazilian farmers data --------
data(brfarmersDiffNet)

expo1 <- threshold(brfarmersDiffNet)
expo2 <- threshold(brfarmersDiffNet^2)

# Computing correlation
cor(expo1,expo2)

# Drawing a qqplot
qqplot(expo1, expo2)

# Working with inverse ------------------------------------------------------
brf2_step <- brfarmersDiffNet^2
brf2_step <- 1/brf2_step

# Removing the first 3 vertex of medInnovationsDiffnet ----------------------
data(medInnovationsDiffNet)

# Using a diffnet object
first3Diffnet <- medInnovationsDiffNet[1:3,,]
medInnovationsDiffNet - first3Diffnet

# Using indexes
medInnovationsDiffNet - 1:3

# Using ids
medInnovationsDiffNet - as.character(1001:1003)

Creates a diffnet class object

Description

diffnet objects contain diffusion networks. With adjacency matrices and time of adoption (toa) vector (or matrix, for multiple behavior diffusion), as its main components, most of the package's functions have methods for this class of objects.

Usage

as_diffnet(graph, ...)

## Default S3 method:
as_diffnet(graph, ...)

## S3 method for class 'networkDynamic'
as_diffnet(graph, toavar, ...)

new_diffnet(
  graph,
  toa,
  t0 = min(toa, na.rm = TRUE),
  t1 = max(toa, na.rm = TRUE),
  vertex.dyn.attrs = NULL,
  vertex.static.attrs = NULL,
  id.and.per.vars = NULL,
  graph.attrs = NULL,
  undirected = getOption("diffnet.undirected"),
  self = getOption("diffnet.self"),
  multiple = getOption("diffnet.multiple"),
  name = "Diffusion Network",
  behavior = NULL
)

## S3 method for class 'diffnet'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  attr.class = c("dyn", "static"),
  ...
)

diffnet.attrs(
  graph,
  element = c("vertex", "graph"),
  attr.class = c("dyn", "static"),
  as.df = FALSE
)

diffnet.attrs(graph, element = "vertex", attr.class = "static") <- value

diffnet.toa(graph)

diffnet.toa(graph, i) <- value

## S3 method for class 'diffnet'
print(x, ...)

nodes(graph)

diffnetLapply(graph, FUN, ...)

## S3 method for class 'diffnet'
str(object, ...)

## S3 method for class 'diffnet'
dimnames(x)

## S3 method for class 'diffnet'
t(x)

## S3 method for class 'diffnet'
dim(x)

is_undirected(x)

## S3 method for class 'diffnet'
is_undirected(x)

## Default S3 method:
is_undirected(x)

is_self(x)

## S3 method for class 'diffnet'
is_self(x)

## Default S3 method:
is_self(x)

is_multiple(x)

## S3 method for class 'diffnet'
is_multiple(x)

## Default S3 method:
is_multiple(x)

is_valued(x)

## S3 method for class 'diffnet'
is_valued(x)

## Default S3 method:
is_valued(x)

Arguments

Details

diffnet objects hold both, static and dynamic vertex attributes. When creating diffnet objects, these can be specified using the arguments vertex.static.attrs and vertex.dyn.attrs; depending on whether the attributes to specify are static or dynamic, netdiffuseR currently supports the following objects:

The last column, Check sorting, lists the variables that the user should specify if he wants the function to check the order of the rows of the attributes (notice that this is not possible for the case of vectors). By providing the name of the vertex id variable, id, and the time period id variable, per, the function makes sure that the attribute data is presented in the right order. See the example below. If the user does not provide the names of the vertex id and time period variables then the function does not check the way the rows are sorted, further it assumes that the data is in the correct order.

The function 'is_undirected' returns TRUE if the network is marked as undirected. In the case of 'diffnet' objects, this information is stored in the 'meta' element as 'undirected'. The default method is to try to find an attribute called 'undirected', i.e., 'attr(x, "undirected")', if no attribute is found, then the function returns 'FALSE'.

The functions 'is_self', 'is_valued', and 'is_multiple' work exactly the same as 'is_undirected'. 'diffnet' networks are not valued.

Value

A list of class diffnet with the following elements:

.

Auxiliary functions

diffnet.attrs Allows retriving network attributes. In particular, by default returns a list of length T with data frames with the following columns:

1. per Indicating the time period to which the observation corresponds.

2. toa Indicating the time of adoption of the vertex.

3. Further columns depending on the vertex and graph attributes.

Each vertex static attributes' are repeated T times in total so that these can be binded (rbind) to dynamic attributes.

When as.df=TRUE, this convenience function is useful as it can be used to create event history (panel data) datasets used for model fitting.

Conversely, the replacement method allows including new vertex or graph attributes either dynamic or static (see examples below).

diffnet.toa(graph) works as an alias of graph$toa. The replacement method, diffnet.toa<- used as diffnet.toa(graph)<-..., is the right way of modifying times of adoption as when doing so it performs several checks on the time ranges, and recalculates adoption and cumulative adoption matrices using toa_mat.

nodes(graph) is an alias for graph$meta$ids.

Author(s)

George G. Vega Yon & Aníbal Olivera M.

See Also

Default options are listed at netdiffuseR-options

Other diffnet methods: %*%(), as.array.diffnet(), c.diffnet(), diffnet-arithmetic, diffnet_index, plot.diffnet(), summary.diffnet()

Other data management functions: edgelist_to_adjmat(), egonet_attrs(), isolated(), survey_to_diffnet()

Examples

# Creating a diffnet object from TOA (time of adoption) ---------------------

# Creating a random graph
set.seed(123)
graph <- rgraph_ba(t=9)
graph <- lapply(1:5, function(x) graph)

# Pretty TOA
names(graph) <- 2001L:2005L
toa <- sample(c(2001L:2005L,NA), 10, TRUE)

# Creating diffnet object
diffnet <- new_diffnet(graph, toa)
diffnet
summary(diffnet)

# Plotting slice 4
plot(diffnet, t=4)

# A diffnet object from TOA of multiple behaviors ---------------------------

# TOA for two behaviors
toa_matrix <- matrix(sample(c(2001L:2005L,NA), 20, TRUE), ncol = 2)

# Creating diffnet object
diffnet_multi <- new_diffnet(graph, toa_matrix)
diffnet_multi
summary(diffnet_multi)

# ATTRIBUTES ----------------------------------------------------------------

# Retrieving attributes
diffnet.attrs(diffnet, "vertex", "static")

# Now as a data.frame (only static)
diffnet.attrs(diffnet, "vertex", "static", as.df = TRUE)

# Now as a data.frame (all of them)
diffnet.attrs(diffnet, as.df = TRUE)
as.data.frame(diffnet) # This is a wrapper

# Unsorted data -------------------------------------------------------------
# Loading example data
data(fakesurveyDyn)

# Creating a diffnet object
fs_diffnet <- survey_to_diffnet(
   fakesurveyDyn, "id", c("net1", "net2", "net3"), "toa", "group",
   timevar = "time", keep.isolates=TRUE, warn.coercion=FALSE)

# Now, we extract the graph data and create a diffnet object from scratch
graph <- fs_diffnet$graph
ids <- fs_diffnet$meta$ids
graph <- Map(function(g) {
  dimnames(g) <- list(ids,ids)
  g
  }, g=graph)
attrs <- diffnet.attrs(fs_diffnet, as.df=TRUE)
toa   <- diffnet.toa(fs_diffnet)

# Lets apply a different sorting to the data to see if it works
n <- nrow(attrs)
attrs <- attrs[order(runif(n)),]

# Now, recreating the old diffnet object (notice -id.and.per.vars- arg)
fs_diffnet_new <- new_diffnet(graph, toa=toa, vertex.dyn.attrs=attrs,
   id.and.per.vars = c("id", "per"))

# Now, retrieving attributes. The 'new one' will have more (repeated)
attrs_new <- diffnet.attrs(fs_diffnet_new, as.df=TRUE)
attrs_old <- diffnet.attrs(fs_diffnet, as.df=TRUE)

# Comparing elements!
tocompare <- intersect(colnames(attrs_new), colnames(attrs_old))
all(attrs_new[,tocompare] == attrs_old[,tocompare], na.rm = TRUE) # TRUE!

# diffnetLapply -------------------------------------------------------------

data(medInnovationsDiffNet)
diffnetLapply(medInnovationsDiffNet, function(x, cumadopt, ...) {sum(cumadopt)})

Infer whether value is dynamic or static.

Description

Intended for internal use only, this function is used in diffnet_index methods.

Usage

diffnet_check_attr_class(value, meta)

Arguments

Value

The value object either as a data frame (if static) or as a list of data frames (if dynamic). If value does not follows the permitted types of diffnet_index, then returns with error.

Indexing diffnet objects (on development)

Description

Access and assign (replace) elements from the adjacency matrices or the vertex attributes data frames.

Usage

## S3 method for class 'diffnet'
x[[name, as.df = FALSE]]

## S3 replacement method for class 'diffnet'
x[[i, j]] <- value

## S3 method for class 'diffnet'
x[i, j, k, drop = FALSE]

## S3 replacement method for class 'diffnet'
x[i, j, k] <- value

Arguments

Details

The [[.diffnet methods provides access to the diffnet attributes data frames, static and dynamic. By providing the name of the corresponding attribute, depending on whether it is static or dynamic the function will return either a data frame–static attributes–or a list of these–dynamic attributes. For the assigning method, ⁠[[<-.diffnet⁠, the function will infer what kind of attribute is by analyzing the dimensions of value, in particular we have the following possible cases:

*: With n\times 1 data.frame/matrix or n length vector.

Other cases will return with error.

In the case of the slices index k, either an integer vector with the positions, a character vector with the labels of the time periods or a logical vector of length T can be used to specify which slices to retrieve. Likewise, indexing vertices works in the same way with the only difference that, instead of time period labels and a logical vector of length T, vertices ids labels and a logical vector of length n should be provided.

When subsetting slices, the function modifies the toa vector as well as the adopt and cumadopt matrices collapsing network tinmming. For example, if a network goes from time 1 to 20 and we set k=3:10, all individuals who adopted prior to time 3 will be set as adopters at time 3, and all individuals who adopted after time 10 will be set as adopters at time 10, changing the adoption and cumulative adoption matrices. Importantly, k have no gaps, and it should be within the graph time period range.

Value

In the case of the assigning methods, a diffnet object. Otherwise, for [[.diffnet a vector extracted from one of the attributes data frames, and for [.diffnet a list of length length(k) with the corresponding [i,j] elements from the adjacency matrix.

Author(s)

George G. Vega Yon

See Also

Other diffnet methods: %*%(), as.array.diffnet(), c.diffnet(), diffnet-arithmetic, diffnet-class, plot.diffnet(), summary.diffnet()

Examples

# Creating a random diffusion network ---------------------------------------
set.seed(111)
graph <- rdiffnet(50,4)

# Accessing to a static attribute
graph[["real_threshold"]]

# Accessing to subsets of the adjacency matrix
graph[1,,1:3, drop=TRUE]
graph[,,1:3, drop=TRUE][[1]]

# ... Now, as diffnet objects (the default)
graph[1,,1:3, drop=FALSE]
graph[,,1:3, drop=FALSE]

# Changing values in the adjacency matrix
graph[1, , , drop=TRUE]
graph[1,,] <- -5
graph[1, , , drop=TRUE]

# Adding attributes (dynamic) -----------------------------------------------
# Preparing the data
set.seed(1122)
x <- rdiffnet(30, 4, seed.p.adopt=.15)

# Calculating exposure, and storing it diffe
expoM <- exposure(x)
expoL <- lapply(seq_len(x$meta$nper), function(x) expoM[,x,drop=FALSE])
expoD <- do.call(rbind, expoL)

# Adding data (all these are equivalent)
x[["expoM"]] <- expoM
x[["expoL"]] <- expoL
x[["expoD"]] <- expoD

# Lets compare
identical(x[["expoM"]], x[["expoL"]]) # TRUE
identical(x[["expoM"]], x[["expoD"]]) # TRUE

Diffusion regression model

Description

A wrapper of glm, this function estimates a lagged regression model of adoption as a function of exposure and other controls as especified by the user.

Usage

diffreg(model, type = c("logit", "probit"))

Arguments

Details

The model must be in the following form:

<diffnet object> ~ exposure + covariate1 + covariate2 + ...

Where exposure can be especified either as a simple term, or as a call to the exposure function, e.g. to compute exposure with a lag of length 2, the formula could be:

<diffnet object> ~ exposure(lags = 2) + covariate1 + covariate2 + ...

When no argument is passed to exposure, the function sets a lag of length 1 by default (see the Lagged regression section).

This is a wrapper of glm. The function does the following steps:

1. Compute exposure by calling exposure on the LHS (dependent variable).

2. Modify the formula so that the model is on adoption as a function of exposure and whatever covariates the user specifies.

3. Selects either "probit" or "logit" and prepares the call to glm. This includes passing the following line:

    subset = ifelse(is.na(toa), TRUE, toa >= per)
    

   This results in including observations that either did not adopted or up to the time of adoption.

4. Estimates the model.

The data passed to glm is obtained by using as.data.frame.diffnet.

Value

An object of class glm.

Lagged regression

The model estimated is a lagged regression model that has two main assumptions:

1. The network is exogenous to the behavior (no selection effect)

2. The influence effect (diffusion) happens in a lagged fasion, hence, exposure is computed lagged.

If either of these two assumptions is not met, then the model becomes endogenous, ans so inference becomes invalid.

In the case of the first assumption, the user can overcome the non-exogeneity problem by providing an alternative network. This can be done by especifying alt.graph in the exposure function so that the network becomes exogenous to the adoption.

Examples

data("medInnovationsDiffNet")

# Default model
ans <- diffreg(
  medInnovationsDiffNet ~ exposure + factor(city) + proage + per)
summary(ans)

Diffusion Network Datasets

Description

Diffusion Network Datasets

Details

The three classic network diffusion datasets included in netdiffuseR are the medical innovation data originally collected by Coleman, Katz & Menzel (1966); the Brazilian Farmers collected as part of the three country study implemented by Everett Rogers (Rogers, Ascroft, & Röling, 1970), and Korean Family Planning data collected by researchers at the Seoul National University's School of Public (Rogers & Kincaid, 1981). The table below summarizes the three datasets:

All datasets include a column called study which is coded as (1) Medical Innovation (2) Brazilian Farmers, (3) Korean Family Planning.

Value

No return value (this manual entry only provides information).

Right censored data

By convention, non-adopting actors are coded as one plus the last observed time of adoption. Prior empirical event history approaches have used this approach (Valente, 2005; Marsden and Podolny, 1990) and studies have shown that omitting such observations leads to biased results (van den Bulte & Iyengar, 2011).

Author(s)

Thomas W. Valente

References

Burt, R. S. (1987). "Social Contagion and Innovation: Cohesion versus Structural Equivalence". American Journal of Sociology, 92(6), 1287–1335. doi:10.1086/228667

Coleman, J., Katz, E., & Menzel, H. (1966). Medical innovation: A diffusion study (2nd ed.). New York: Bobbs-Merrill

Granovetter, M., & Soong, R. (1983). Threshold models of diffusion and collective behavior. The Journal of Mathematical Sociology, 9(October 2013), 165–179. doi:10.1080/0022250X.1983.9989941

Rogers, E. M., Ascroft, J. R., & Röling, N. (1970). Diffusion of Innovation in Brazil, Nigeria, and India. Unpublished Report. Michigan State University, East Lansing.

Everett M. Rogers, & Kincaid, D. L. (1981). Communication Networks: Toward a New Paradigm for Research. (C. Macmillan, Ed.). New York; London: Free Press.

Mardsen, P., & Podolny, J. (1990). Dynamic Analysis of Network Diffusion Processes, J. Weesie, H. Flap, eds. Social Networks Through Time, 197–214.

Marsden, P. V., & Friedkin, N. E. (1993). Network Studies of Social Influence. Sociological Methods & Research, 22(1), 127–151. doi:10.1177/0049124193022001006

Van den Bulte, C., & Iyengar, R. (2011). Tricked by Truncation: Spurious Duration Dependence and Social Contagion in Hazard Models. Marketing Science, 30(2), 233–248. doi:10.1287/mksc.1100.0615

Valente, T. W. (1991). Thresholds and the critical mass: Mathematical models of the diffusion of innovations. University of Southern California.

Valente, T. W. (1995). "Network models of the diffusion of innovations" (2nd ed.). Cresskill N.J.: Hampton Press.

Valente, T. W. (2005). Network Models and Methods for Studying the Diffusion of Innovations. In Models and Methods in Social Network Analysis, Volume 28 of Structural Analysis in the Social Sciences (pp. 98–116). New York: Cambridge University Press.

See Also

Other diffusion datasets: brfarmers, brfarmersDiffNet, fakeDynEdgelist, fakeEdgelist, fakesurvey, fakesurveyDyn, kfamily, kfamilyDiffNet, medInnovations, medInnovationsDiffNet

Creates a heatmap based on a graph layout and a vertex attribute

Description

Using bi-dimensional kernel smoothers, creates a heatmap based on a graph layout and colored accordingly to x. This visualization technique is intended to be used with large graphs.

Usage

diffusionMap(graph, ...)

diffmap(graph, ...)

## Default S3 method:
diffusionMap(
  graph,
  x,
  x.adj = round_to_seq,
  layout = NULL,
  jitter.args = list(),
  kde2d.args = list(n = 100),
  sharp.criter = function(x, w) {
     wvar(x, w) > (max(x, na.rm = TRUE) - min(x, na.rm
    = TRUE))^2/12
 },
  ...
)

## S3 method for class 'diffnet'
diffusionMap(graph, slice = nslices(graph), ...)

## S3 method for class 'diffnet_diffmap'
image(x, ...)

## S3 method for class 'diffnet_diffmap'
print(x, ...)

## S3 method for class 'diffnet_diffmap'
plot(x, y = NULL, ...)

Arguments

Details

The image is created using the function kde2d from the MASS package. The complete algorithm follows:

1. x is coerced into integer and the range is adjusted to start from 1. NA are replaced by zero.

2. If no layout is passed, layout is computed using layout_nicely from igraph

3. Then, a kde2d map is computed for each level of x. The resulting matrices are added up as a weighted sum. This only holds if at the cell level the function sharp.criter returns FALSE.

4. The jitter function is applied to the repeated coordinates.

5. 2D kernel is computed using kde2d over the coordinates.

The function sharp.criter must take two values, a vector of levels and a vector of weights. It must return a logical scalar with value equal to TRUE when a randomization at the cell level must be done, in which case the final value of the cell is chosen using sample(x, 1, prob=w).

The resulting matrix can be passed to image or similar.

The argument x.adj uses by default the function round_to_seq which basically maps x to a fix length sequence of numbers such that x.adj(x) resembles an integer sequence.

Value

A list of class diffnet_diffmap

Author(s)

George G. Vega Yon

References

Vega Yon, George G., and Valente, Thomas W., Visualizing Large Annotated Networks as Heatmaps using Weighted Averages based on Kernel Smoothers (Working paper).

See Also

Other visualizations: dgr(), drawColorKey(), grid_distribution(), hazard_rate(), plot_adopters(), plot_diffnet(), plot_diffnet2(), plot_infectsuscep(), plot_threshold(), rescale_vertex_igraph()

Examples

# Example with a random graph --------------------------------------------------

set.seed(1231)

# Random scale-free diffusion network
x <- rdiffnet(500, 4, seed.graph="scale-free", seed.p.adopt = .025,
                           rewire = FALSE, seed.nodes = "central",
                           rgraph.arg=list(self=FALSE, m=4),
                           threshold.dist = function(id) runif(1,.2,.4))

# Diffusion map (no random toa)
dm0 <- diffusionMap(x, kde2d.args=list(n=150, h=.5), layout=igraph::layout_with_fr)

# Random
diffnet.toa(x) <- sample(x$toa, size = nnodes(x))

# Diffusion map (random toa)
dm1 <- diffusionMap(x, layout = dm0$coords, kde2d.args=list(n=150, h=.5))

oldpar <- par(no.readonly = TRUE)
col <- colorRampPalette(blues9)(100)
par(mfrow=c(1,2), oma=c(1,0,0,0))
image(dm0, col=col, main="Non-random Times of Adoption\nAdoption from the core.")
image(dm1, col=col, main="Random Times of Adoption")
par(mfrow=c(1,1))
mtext("Both networks have the same distribution on times of adoption", 1,
      outer = TRUE)
par(oldpar)

# Example with Brazilian Farmers --------------------------------------------
dn <- brfarmersDiffNet

# Setting last TOA as NA
diffnet.toa(dn)[dn$toa == max(dn$toa)] <-
  NA

# Coordinates
coords <- sna::gplot.layout.fruchtermanreingold(
  as.matrix(dn$graph[[1]]), layout.par=NULL
)

# Plotting diffusion
plot_diffnet2(dn, layout=coords, vertex.size = 300)

# Adding diffusion map
out <- diffusionMap(dn, layout=coords, kde2d.args=list(n=100, h=50))
col <- adjustcolor(colorRampPalette(c("white","lightblue", "yellow", "red"))(100),.5)
with(out$map, .filled.contour(x,y,z,pretty(range(z), 100),col))

Draw a color key in the current device

Description

Draw a color key in the current device

Usage

drawColorKey(
  x,
  tick.marks = pretty_within(x),
  labels = tick.marks,
  main = NULL,
  key.pos = c(0.925, 0.975, 0.05, 0.95),
  pos = 2,
  nlevels = length(tick.marks),
  color.palette = viridisLite::viridis(nlevels),
  tick.width = c(0.01, 0.0075),
  add.box = TRUE,
  na.col = NULL,
  na.height = 0.1,
  na.lab = "n/a",
  ...
)

Arguments

Value

Invisible NULL.

Author(s)

George G. Vega Yon

See Also

Other visualizations: dgr(), diffusionMap(), grid_distribution(), hazard_rate(), plot_adopters(), plot_diffnet(), plot_diffnet2(), plot_infectsuscep(), plot_threshold(), rescale_vertex_igraph()

Examples

set.seed(166)
x <- rnorm(100)
col <- colorRamp(c("lightblue", "yellow", "red"))((x - min(x))/(max(x) - min(x)))
col <- rgb(col, maxColorValue = 255)
plot(x, col=col, pch=19)
drawColorKey(x, nlevels = 100, border="transparent",
 main="Key\nLike A\nBoss")

Conversion between adjacency matrix and edgelist

Description

Generates adjacency matrix from an edgelist and vice versa.

Usage

edgelist_to_adjmat(
  edgelist,
  w = NULL,
  t0 = NULL,
  t1 = NULL,
  t = NULL,
  simplify = TRUE,
  undirected = getOption("diffnet.undirected"),
  self = getOption("diffnet.self"),
  multiple = getOption("diffnet.multiple"),
  keep.isolates = TRUE,
  recode.ids = TRUE
)

adjmat_to_edgelist(
  graph,
  undirected = getOption("diffnet.undirected", FALSE),
  keep.isolates = getOption("diffnet.keep.isolates", TRUE)
)

Arguments

Details

When converting from edglist to adjmat the function will recode the edgelist before starting. The user can keep track after the recording by checking the resulting adjacency matrices' row.names. In the case that the user decides skipping the recoding (because wants to keep vertices index numbers, implying that the resulting graph will have isolated vertices), he can override this by setting recode.ids=FALSE (see example).

When multiple edges are included, multiple=TRUE,each vertex between \{i,j\} will be counted as many times it appears in the edgelist. So if a vertex \{i,j\} appears 2 times, the adjacency matrix element (i,j) will be 2.

Edges with incomplete information (missing data on w or times) are not included on the graph. Incomplete cases are tagged using complete.cases and can be retrieved by the user by accessing the attribute incomplete.

Were the case that either ego or alter are missing (i.e. NA values), the function will either way include the non-missing vertex. See below for an example of this.

The function performs several checks before starting to create the adjacency matrix. These are:

• Dimensions of the inputs, such as number of columns and length of vectors

• Having complete cases. If anly edge has a non-numeric value such as NAs or NULL in either times or w, it will be removed. A full list of such edges can be retrieved from the attribute incomplete

• Nodes and times ids coding

recode.ids=FALSE is useful when the vertices ids have already been coded. For example, after having use adjmat_to_edgelist, ids are correctly encoded, so when going back (using edgelist_to_adjmat) recode.ids should be FALSE.

Value

In the case of edgelist_to_adjmat either an adjacency matrix (if times is NULL) or an array of these (if times is not null). For adjmat_to_edgelist the output is an edgelist with the following columns:

Author(s)

George G. Vega Yon & Thomas W. Valente

See Also

Other data management functions: diffnet-class, egonet_attrs(), isolated(), survey_to_diffnet()

Examples

# Base data
set.seed(123)
n <- 5
edgelist <- rgraph_er(n, as.edgelist=TRUE, p=.2)[,c("ego","alter")]
times <- sample.int(3, nrow(edgelist), replace=TRUE)
w <- abs(rnorm(nrow(edgelist)))

# Simple example
edgelist_to_adjmat(edgelist)
edgelist_to_adjmat(edgelist, undirected = TRUE)

# Using w
edgelist_to_adjmat(edgelist, w)
edgelist_to_adjmat(edgelist, w, undirected = TRUE)

# Using times
edgelist_to_adjmat(edgelist, t0 = times)
edgelist_to_adjmat(edgelist, t0 = times, undirected = TRUE)

# Using times and w
edgelist_to_adjmat(edgelist, t0 = times, w = w)
edgelist_to_adjmat(edgelist, t0 = times, undirected = TRUE, w = w)

# Not recoding ----------------------------------------------------
# Notice that vertices 3, 4 and 5 are not present in this graph.
graph <- matrix(c(
 1,2,6,
 6,6,7
), ncol=2)

# Generates an adjmat of size 4 x 4
edgelist_to_adjmat(graph)

# Generates an adjmat of size 7 x 7
edgelist_to_adjmat(graph, recode.ids=FALSE)

# Dynamic with spells -------------------------------------------------------
edgelist <- rbind(
   c(1,2,NA,1990),
   c(2,3,NA,1991),
   c(3,4,1991,1992),
   c(4,1,1992,1993),
   c(1,2,1993,1993)
)

graph <- edgelist_to_adjmat(edgelist[,1:2], t0=edgelist[,3], t1=edgelist[,4])

# Creating a diffnet object with it so we can apply the plot_diffnet function
diffnet <- as_diffnet(graph, toa=1:4)
plot_diffnet(diffnet, label=rownames(diffnet))

# Missing alter in the edgelist ---------------------------------------------
data(fakeEdgelist)

# Notice that edge 202 is isolated
fakeEdgelist

# The function still includes vertex 202
edgelist_to_adjmat(fakeEdgelist[,1:2])

edgelist

Compute ego/alter edge coordinates considering alter's size and aspect ratio

Description

Given a graph, vertices' positions and sizes, calculates the absolute positions of the endpoints of the edges considering the plot's aspect ratio.

Usage

edges_coords(
  graph,
  toa,
  x,
  y,
  vertex_cex,
  undirected = TRUE,
  no_contemporary = TRUE,
  dev = as.numeric(c()),
  ran = as.numeric(c()),
  curved = as.logical(c())
)

Arguments

Details

In order to make the plot's visualization more appealing, this function provides a straight forward way of computing the tips of the edges considering the aspect ratio of the axes range. In particular, the following corrections are made at the moment of calculating the egdes coords:

• Instead of using the actual distance between ego and alter, a relative one is calculated as follows

  d'=\left[(x_0-x_1)^2 + (y_0' - y_1')^2\right]^\frac{1}{2}

  where % y_i'=y_i\times\frac{\max x - \min x}{\max y - \min y}

• Then, for the relative elevation angle, alpha, the relative distance d' is used, \alpha'=\arccos\left( (x_0 - x_1)/d' \right)

• Finally, the edge's endpoint's (alter) coordinates are computed as follows:

  % x_1' = x_1 + \cos(\alpha')\times v_1

  % y_1' = y_1 -+ \sin(\alpha')\times v_1 \times\frac{\max y - \min y}{\max x - \min x}

  Where v_1 is alter's size in terms of the x-axis, and the sign of the second term in y_1' is negative iff y_0 < y_1.

The same process (with sign inverted) is applied to the edge starting piont. The resulting values, x_1',y_1' can be used with the function arrows. This is the workhorse function used in plot_threshold.

The dev argument provides a reference to rescale the plot accordingly to the device, and former, considering the size of the margins as well (this can be easily fetched via par("pin"), plot area in inches).

On the other hand, ran provides a reference for the adjustment according to the range of the data, this is range(x)[2] - range(x)[1] and range(y)[2] - range(y)[1] respectively.

Value

A numeric matrix of size m\times 5 with the following columns:

With m as the number of resulting edges.

Examples

# --------------------------------------------------------------------------
data(medInnovationsDiffNet)
library(sna)

# Computing coordinates
set.seed(79)
coords <- sna::gplot(as.matrix(medInnovationsDiffNet$graph[[1]]))

# Getting edge coordinates
vcex <- rep(1.5, nnodes(medInnovationsDiffNet))
ecoords <- edges_coords(
  medInnovationsDiffNet$graph[[1]],
  diffnet.toa(medInnovationsDiffNet),
  x = coords[,1], y = coords[,2],
  vertex_cex = vcex,
  dev = par("pin")
  )

ecoords <- as.data.frame(ecoords)

# Plotting
symbols(coords[,1], coords[,2], circles=vcex,
  inches=FALSE, xaxs="i", yaxs="i")

with(ecoords, arrows(x0,y0,x1,y1, length=.1))

Computes variance of Y at ego level

Description

Computes variance of Y at ego level

Usage

ego_variance(graph, Y, funname, all = FALSE)

Arguments

Details

For each vertex i the variance is computed as follows

% (\sum_j a_{ij})^{-1}\sum_j a_{ij} \left[f(y_i,y_j) - f_i\right]^2

Where a_{ij} is the ij-th element of graph, f is the function specified in funname, and, if all=FALSE f_i = \sum_j a_{ij}f(y_i,y_j)^2/\sum_ja_{ij}, otherwise f_i = f_j = \frac{1}{n^2}\sum_{i,j}f(y_i,y_j)

This is an auxiliary function for struct_test. The idea is to compute an adjusted measure of disimilarity between vertices, so the closest in terms of f is i to its neighbors, the smaller the relative variance.

Value

A numeric vector of length n.

See Also

struct_test

Other statistics: bass, classify_adopters(), cumulative_adopt_count(), dgr(), exposure(), hazard_rate(), infection(), moran(), struct_equiv(), threshold(), vertex_covariate_dist()

Retrieve alter's attributes (network effects)

Description

For a given set of vertices V, retrieves each vertex's alter's attributes. This function enables users to calculate exposure on variables other than the attribute that is diffusing. Further, it enables the specification of alternative functions to use to characterize ego's personal network including calculating the mean, maximum, minimum, median, or sum of the alters' attributes. These measures may be static or dynamic over the interval of diffusion and they may be binary or valued.

Usage

egonet_attrs(
  graph,
  attrs,
  V = NULL,
  direction = "outgoing",
  fun = function(x) x,
  as.df = FALSE,
  self = getOption("diffnet.self"),
  valued = getOption("diffnet.valued"),
  ...
)

Arguments

Details

By indexing inner/outer edges, this function retrieves ego network attributes for all v \in V, which by default is the complete set of vertices in the graph.

When as.df=TRUE the function returns a data.frame of size (|V|\times T)\times k where T is the number of time periods and k is the number of columns generated by the function.

The function can be used to create network effects as those in the RSiena package. The difference here is that the definition of the statistic directly relies on the user. For example, in the RSiena package, the dyadic covariate effect 37. covariate (centered) main effect (X)

% s_{i37}(x) = \sum_j x_{ij}(w_{ij}-\bar w)

Which, having a diffnet object with attributes named x and w, can be calculated as

    egonet_attrs(diffnet, as.df=TRUE, fun=function(dat) {
     sum(dat[, "x"]*(dat[, "w"] - mean(dat[, "w"])))
    })
    

Furthermore, we could use the median centered instead, for example

    egonet_attrs(diffnet, as.df=TRUE, fun=function(dat) {
     sum(dat[, "x"]*(dat[, "w"] - median(dat[, "w"])))
    })
    

Where for each i, dat will be a matrix with as many rows as individuals in his egonetwork. Such matrix holds the column names of the attributes in the network.

When self = TRUE, it will include ego's attributes, regardless the network has loops or not.

Value

A list with ego alters's attributes. By default, if the graph is static, the output is a list of length length(V) with matrices having the following columns:

On the other hand, if graph is dynamic, the output is list of length T of lists of length length(V) with data frames having the following columns:

Author(s)

George G. Vega Yon

See Also

Other data management functions: diffnet-class, edgelist_to_adjmat(), isolated(), survey_to_diffnet()

Examples

# Simple example with diffnet -----------------------------------------------
set.seed(1001)
diffnet <- rdiffnet(150, 5, seed.graph="small-world")

# Adding attributes
indeg <- dgr(diffnet, cmode="indegree")
head(indeg)
diffnet[["indegree"]] <- indeg

# Retrieving egonet's attributes (vertices 1 and 20)
egonet_attrs(diffnet, V=c(1,20))

# Example with a static network ---------------------------------------------

set.seed(1231)
n <- 20
net <- rgraph_ws(n = n, k = 4, p = .5)
someattr <- matrix(rnorm(n * 2), ncol= 2, dimnames = list(NULL, c("a", "b")))

# Maximum of -a- in ego network
ans <- egonet_attrs(net, someattr, fun = function(x) max(x[,"a"]))
ans

# checking it worked, taking a look at node 1, 2, and 3
max(someattr[which(net[1,] == 1),"a"]) == ans[1] # TRUE
max(someattr[which(net[2,] == 1),"a"]) == ans[2] # TRUE
max(someattr[which(net[3,] == 1),"a"]) == ans[3] # TRUE

Ego exposure

Description

Calculates exposure to adoption over time via multiple different types of weight matrices. The basic model is exposure to adoption by immediate neighbors (outdegree) at the time period prior to ego’s adoption. This exposure can also be based on (1) incoming ties, (2) structural equivalence, (3) indirect ties, (4) network-metric weighted (e.g., central nodes have more influence), and (5) attribute-weighted (e.g., based on homophily or tie strength).

Usage

exposure(
  graph,
  cumadopt,
  attrs = NULL,
  alt.graph = NULL,
  outgoing = getOption("diffnet.outgoing", TRUE),
  valued = getOption("diffnet.valued", FALSE),
  normalized = TRUE,
  groupvar = NULL,
  self = getOption("diffnet.self"),
  lags = 0L,
  ...
)

Arguments

Details

Exposure is calculated as follows:

% E_t = \left(S_t \times \left[x_t \circ A_t\right]\right) / (S_t \times x_t) %

Where S_t is the graph in time t, x_t is an attribute vector of size n at time t, A_t is the t-th column of the cumulative adopters matrix (a vector of length n with a_{ti}=1 if i has adopted at or prior to t), \circ is the kronecker product (element-wise), and \times is the matrix product.

By default the graph used for this calculation, S, is the social network. Alternatively, in the case of diffnet objects, the user can provide an alternative graph using alt.graph. An example of this would be using 1/SE, the element-wise inverse of the structural equivalence matrix (see example below). Furthermore, if alt.graph="se", the inverse of the structural equivalence is computed via struct_equiv and used instead of the provided graph. Notice that when using a valued graph the option valued should be equal to TRUE, this check is run automatically when running the model using structural equivalence.

If the alt.graph is static, then the function will warn about it and will recycle the graph to compute exposure at each time point.

An important remark is that when calculating structural equivalence the function assumes that this is to be done to the entire graph regardless of disconnected communities (as in the case of the medical innovations data set). Hence, structural equivalence for individuals for two different communites may not be zero. If the user wants to calculate structural equivalence separately by community, he should create different diffnet objects and do so (see example below). Alternatively, for the case of diffnet objects, by using the option groupvar (see struct_equiv), the user can provide the function with the name of a grouping variable–which should one in the set of static vertex attributes–so that the algorithm is done by group (or community) instead of in an aggregated way.

If the user does not specifies a particular weighting attribute in attrs, the function sets this as a matrix of ones. Otherwise the function will return an attribute weighted exposure. When graph is of class diffnet, attrs can be a character scalar specifying the name of any of the graph's attributes, both dynamic and static. See the examples section for a demonstration using degree.

When outgoing=FALSE, S is replaced by its transposed, so in the case of a social network exposure will be computed based on the incoming ties.

If normalize=FALSE then denominator, S_t \times x_t, is not included. This can be useful when, for example, exposure needs to be computed as a count instead of a proportion. A good example of this can be found at the examples section of the function rdiffnet.

Value

A matrix of size n\times T with exposure for each node.

Author(s)

George G. Vega Yon, Thomas W. Valente, and Aníbal Olivera M.

References

Burt, R. S. (1987). "Social Contagion and Innovation: Cohesion versus Structural Equivalence". American Journal of Sociology, 92(6), 1287. doi:10.1086/228667

Valente, T. W. (1995). "Network models of the diffusion of innovations" (2nd ed.). Cresskill N.J.: Hampton Press.

See Also

Other statistics: bass, classify_adopters(), cumulative_adopt_count(), dgr(), ego_variance(), hazard_rate(), infection(), moran(), struct_equiv(), threshold(), vertex_covariate_dist()

Examples

# Calculating lagged exposure -----------------------------------------------

set.seed(8)
graph <- rdiffnet(20, 4)

expo0 <- exposure(graph)
expo1 <- exposure(graph, lags = 1)

# These should be equivalent
stopifnot(all(expo0[, -4] == expo1[, -1])) # No stop!


# Calculating the exposure based on Structural Equivalence ------------------
set.seed(113132)
graph <- rdiffnet(100, 4)

SE <- lapply(struct_equiv(graph), "[[", "SE")
SE <- lapply(SE, function(x) {
   x <- 1/x
   x[!is.finite(x)] <- 0
   x
})


# These three lines are equivalent to:
expo_se2 <- exposure(graph, alt.graph="se", valued=TRUE)
# Notice that we are setting valued=TRUE, but this is not necesary since when
# alt.graph = "se" the function checks this to be setted equal to TRUE

# Weighted Exposure using degree --------------------------------------------
eDE <- exposure(graph, attrs=dgr(graph))

# Which is equivalent to
graph[["deg"]] <- dgr(graph)
eDE2 <- exposure(graph, attrs="deg")

# Comparing using incoming edges -------------------------------------------
eIN <- exposure(graph, outgoing=FALSE)

# Structral equivalence for different communities ---------------------------
data(medInnovationsDiffNet)

# Only using 4 time slides, this is for convenience
medInnovationsDiffNet <- medInnovationsDiffNet[, , 1:4]

# METHOD 1: Using the c.diffnet method:

# Creating subsets by city
cities <- unique(medInnovationsDiffNet[["city"]])

diffnet <- medInnovationsDiffNet[medInnovationsDiffNet[["city"]] == cities[1]]
diffnet[["expo_se"]] <- exposure(diffnet, alt.graph="se", valued=TRUE)

for (v in cities[-1]) {
   diffnet_v <- medInnovationsDiffNet[medInnovationsDiffNet[["city"]] == v]
   diffnet_v[["expo_se"]] <- exposure(diffnet_v, alt.graph="se", valued=TRUE)
   diffnet <- c(diffnet, diffnet_v)
}

# We can set the original order (just in case) of the data
diffnet <- diffnet[medInnovationsDiffNet$meta$ids]
diffnet

# Checking everything is equal
test <- summary(medInnovationsDiffNet, no.print=TRUE) ==
   summary(diffnet, no.print=TRUE)

stopifnot(all(test[!is.na(test)]))

# METHOD 2: Using the 'groupvar' argument
# Further, we can compare this with using the groupvar
diffnet[["expo_se2"]] <- exposure(diffnet, alt.graph="se",
   groupvar="city", valued=TRUE)

# These should be equivalent
test <- diffnet[["expo_se", as.df=TRUE]] == diffnet[["expo_se2", as.df=TRUE]]
stopifnot(all(test[!is.na(test)]))

# METHOD 3: Computing exposure, rbind and then adding it to the diffnet object
expo_se3 <- NULL
for (v in unique(cities))
   expo_se3 <- rbind(
     expo_se3,
     exposure(
       diffnet[diffnet[["city"]] == v],
       alt.graph = "se", valued=TRUE
     ))

# Just to make sure, we sort the rows
expo_se3 <- expo_se3[diffnet$meta$ids,]

diffnet[["expo_se3"]] <- expo_se3

test <- diffnet[["expo_se", as.df=TRUE]] == diffnet[["expo_se3", as.df=TRUE]]
stopifnot(all(test[!is.na(test)]))


# METHOD 4: Using the groupvar in struct_equiv
se <- struct_equiv(diffnet, groupvar="city")
se <- lapply(se, "[[", "SE")
se <- lapply(se, function(x) {
   x <- 1/x
   x[!is.finite(x)] <- 0
   x
})

diffnet[["expo_se4"]] <- exposure(diffnet, alt.graph=se, valued=TRUE)

test <- diffnet[["expo_se", as.df=TRUE]] == diffnet[["expo_se4", as.df=TRUE]]
stopifnot(all(test[!is.na(test)]))


# Examples for multi-diffusion ---------------------------

# Running a multi-diffusion simulation, with q=2 behaviors
set.seed(999)
n <- 40; t <- 5; q <- 2;
graph <- rgraph_ws(n, t, p=.3)
seed_prop_adopt <- rep(list(0.1), q)

diffnet <- rdiffnet(seed.graph = graph, t = t, seed.p.adopt = seed_prop_adopt)

# Getting the cumulative adoption array of dims n x T x q
cumadopt_2 <- diffnet$cumadopt  # list of matrices
cumadopt_2 <- array(unlist(cumadopt_2), dim = c(n, t, q))

expo2 <- exposure(diffnet$graph, cumadopt = cumadopt_2)

# With an attribute --

X <- matrix(runif(n * t), nrow = n, ncol = t) # matrix n x T
ans3 <- exposure(diffnet$graph, cumadopt = cumadopt_2, attrs=X)

X <- array(runif(n * t * q), dim = c(n, t, q)) # array n x T x q
ans4 <- exposure(diffnet$graph, cumadopt = cumadopt_2, attrs=X)

# Exposure based on Structural Equivalence --

diffnet_1 <- split_behaviors(diffnet)[[1]]
se <- struct_equiv(diffnet)
se <- lapply(se, function(x) {
  ans <- methods::as(x$SE, "dgCMatrix")
    ans@x <- 1/(ans@x + 1e-20)
      ans
      })
ans6 <- exposure(diffnet, cumadopt = cumadopt_2, alt.graph = se, valued=TRUE)

Fake dynamic edgelist

Description

A data frame used for examples in reading edgelist format networks. This edgelist can be merged with the dataset fakesurveyDyn.

Format

A data frame with 22 rows and 4 variables

ego

Nominating individual

alter

Nominated individual

value

Strength of the tie

time

Integer with the time of the spell

Author(s)

George G. Vega Yon

Source

Generated for the package

See Also

Other diffusion datasets: brfarmers, brfarmersDiffNet, diffusion-data, fakeEdgelist, fakesurvey, fakesurveyDyn, kfamily, kfamilyDiffNet, medInnovations, medInnovationsDiffNet

Fake static edgelist

Description

A data frame used for examples in reading edgelist format networks. This edgelist can be merged with the dataset fakesurvey.

Format

A data frame with 11 rows and 3 variables

ego

Nominating individual

alter

Nominated individual

value

Strength of the tie

Author(s)

George G. Vega Yon

Source

Generated for the package

See Also

Other diffusion datasets: brfarmers, brfarmersDiffNet, diffusion-data, fakeDynEdgelist, fakesurvey, fakesurveyDyn, kfamily, kfamilyDiffNet, medInnovations, medInnovationsDiffNet

Fake survey data

Description

This data frame is used to ilustrate some of the functions of the package, in particular, the survey_to_diffnet function. This dataset can be merged with the fakeEdgelist.

Format

A data frame with 9 rows and 9 variables

id

Unique id at group level

toa

Time of adoption

group

Group id

net1

Network nomination 1

net2

Network nomination 2

net3

Network nomination 3

age

Age of the respondent

gender

Gende of the respondent

note

Descroption of the respondent

Author(s)

George G. Vega Yon

Source

Generated for the package.

See Also

Other diffusion datasets: brfarmers, brfarmersDiffNet, diffusion-data, fakeDynEdgelist, fakeEdgelist, fakesurveyDyn, kfamily, kfamilyDiffNet, medInnovations, medInnovationsDiffNet

Fake longitudinal survey data

Description

This data frame is used to ilustrate some of the functions of the package, in particular, the survey_to_diffnet function. This dataset can be merged with the fakeDynEdgelist.

Format

A data frame with 18 rows and 10 variables

id

Unique id at group level

toa

Time of adoption

group

Group id

net1

Network nomination 1

net2

Network nomination 2

net3

Network nomination 3

age

Age of the respondent

gender

Gende of the respondent

note

Descroption of the respondent

time

Timing of the wave

Author(s)

George G. Vega Yon

Source

Generated for the package.

See Also

Other diffusion datasets: brfarmers, brfarmersDiffNet, diffusion-data, fakeDynEdgelist, fakeEdgelist, fakesurvey, kfamily, kfamilyDiffNet, medInnovations, medInnovationsDiffNet

Distribution over a grid

Description

Distribution of pairs over a grid of fix size.

Usage

grid_distribution(x, y, nlevels = 100L)

Arguments

Details

This function ment for internal use only.

Value

Returns a list with three elements

Examples

# Generating random vectors of size 100
x <- rnorm(100)
y <- rnorm(100)

# Calculating distribution
grid_distribution(x,y,20)

See Also

Used by plot_infectsuscep

Other visualizations: dgr(), diffusionMap(), drawColorKey(), hazard_rate(), plot_adopters(), plot_diffnet(), plot_diffnet2(), plot_infectsuscep(), plot_threshold(), rescale_vertex_igraph()

Network Hazard Rate

Description

The hazard rate is the instantaneous probability of adoption at each time representing the likelihood members will adopt at that time (Allison 1984). The shape of the hazard rate indicates the pattern of new adopters over time. Rapid diffusion with convex cumulative adoption curves will have hazard functions that peak early and decay over time whereas slow concave cumulative adoption curves will have hazard functions that are low early and rise over time. Smooth hazard curves indicate constant adoption whereas those that oscillate indicate variability in adoption behavior over time.

Usage

hazard_rate(obj, no.plot = FALSE, include.grid = TRUE, ...)

plot_hazard(x, ...)

## S3 method for class 'diffnet_hr'
plot(
  x,
  y = NULL,
  main = "Hazard Rate",
  xlab = "Time",
  ylab = "Hazard Rate",
  type = "b",
  include.grid = TRUE,
  bg = "lightblue",
  pch = 21,
  add = FALSE,
  ylim = c(0, 1),
  ...
)

Arguments

Details

This function computes hazard rate, plots it and returns the hazard rate vector invisible (so is not printed on the console). For t>1, hazard rate is calculated as

\frac{q_t - q_{t-1}}{n - q_{t-1}}

where q_i is the number of adopters in time t, and n is the number of vertices in the graph.

In survival analysis, hazard rate is defined formally as

% \lambda(t)=\lim_{h\to +0}\frac{F(t+h)-F(t)}{h}\frac{1}{1-F(t)} %

Then, by approximating h=1, we can rewrite the equation as

% \lambda(t)=\frac{F(t+1)-F(t)}{1-F(t)} %

Furthermore, we can estimate F(t), the probability of not having adopted the innovation in time t, as the proportion of adopters in that time, this is F(t) \sim q_t/n, so now we have

% \lambda(t)=\frac{q_{t+1}/n-q_t/n}{1-q_t/n} = \frac{q_{t+1} - q_t}{n - q_t} %

As showed above.

The plot_hazard function is an alias for the plot.diffnet_hr method.

Value

A row vector of size T with hazard rates for t>1 of class diffnet_hr. The class of the object is only used by the S3 plot method.

Author(s)

George G. Vega Yon & Thomas W. Valente

References

Allison, P. (1984). Event history analysis regression for longitudinal event data. Beverly Hills: Sage Publications.

Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). Cambridge: MIT Press.

See Also

Other statistics: bass, classify_adopters(), cumulative_adopt_count(), dgr(), ego_variance(), exposure(), infection(), moran(), struct_equiv(), threshold(), vertex_covariate_dist()

Other visualizations: dgr(), diffusionMap(), drawColorKey(), grid_distribution(), plot_adopters(), plot_diffnet(), plot_diffnet2(), plot_infectsuscep(), plot_threshold(), rescale_vertex_igraph()

Examples

# Creating a random vector of times of adoption
toa <- sample(2000:2005, 20, TRUE)

# Computing cumulative adoption matrix
cumadopt <- toa_mat(toa)$cumadopt

# Visualizing the hazard rate
hazard_rate(cumadopt)

Coercion between graph classes

Description

Coercion between graph classes

Usage

diffnet_to_igraph(graph, slices = 1:nslices(graph))

igraph_to_diffnet(
  graph = NULL,
  graph.list = NULL,
  toavar,
  t0 = NULL,
  t1 = NULL,
  ...
)

Arguments

Value

Either a list of length(slices) igraph (diffnet_to_igraph), or a diffnet object (igraph_to_diffnet) objects.

See Also

Other Foreign: network, read_pajek(), read_ucinet_head()

Examples

# Reading the medical innovation data into igraph --------------------------
x <- diffnet_to_igraph(medInnovationsDiffNet[,,1:4])

# Fetching the times of adoption
igraph::vertex_attr(x[[1]], "toa")

Susceptibility and Infection

Description

Calculates infectiousness and susceptibility for each node in the graph

Usage

infection(
  graph,
  toa,
  t0 = NULL,
  normalize = TRUE,
  K = 1L,
  r = 0.5,
  expdiscount = FALSE,
  valued = getOption("diffnet.valued", FALSE),
  outgoing = getOption("diffnet.outgoing", TRUE)
)

susceptibility(
  graph,
  toa,
  t0 = NULL,
  normalize = TRUE,
  K = 1L,
  r = 0.5,
  expdiscount = FALSE,
  valued = getOption("diffnet.valued", FALSE),
  outgoing = getOption("diffnet.outgoing", TRUE)
)

Arguments

Details

Normalization, normalize=TRUE, is applied by dividing the resulting number from the infectiousness/susceptibility stat by the number of individuals who adopted the innovation at time t.

Given that node i adopted the innovation in time t, its Susceptibility is calculated as follows

S_i = \frac{% \sum_{k=1}^K\sum_{j=1}^n x_{ij(t-k+1)}z_{j(t-k)}\times \frac{1}{w_k}}{% \sum_{k=1}^K\sum_{j=1}^n x_{ij(t-k+1)}z_{j(1\leq t \leq t-k)} \times \frac{1}{w_k} }\qquad \mbox{for }i,j=1,\dots,n\quad i\neq j

where x_{ij(t-k+1)} is 1 whenever there's a link from i to j at time t-k+1, z_{j(t-k)} is 1 whenever individual j adopted the innovation at time t-k, z_{j(1\leq t \leq t-k)} is 1 whenever j had adopted the innovation up to t-k, and w_k is the discount rate used (see below).

Similarly, infectiousness is calculated as follows

I_i = \frac{% \sum_{k=1}^K \sum_{j=1}^n x_{ji(t+k-1)}z_{j(t+k)}\times \frac{1}{w_k}}{% \sum_{k=1}^K \sum_{j=1}^n x_{ji(t+k-1)}z_{j(t+k\leq t \leq T)}\times \frac{1}{w_k} }\qquad \mbox{for }i,j=1,\dots,n\quad i\neq j

It is worth noticing that, as we can see in the formulas, while susceptibility is from alter to ego, infection is from ego to alter.

When outgoing=FALSE the algorithms are based on incoming edges, this is the adjacency matrices are transposed swapping the indexes (i,j) by (j,i). This can be useful for some users.

Finally, by default both are normalized by the number of individuals who adopted the innovation in time t-k. Thus, the resulting formulas, when normalize=TRUE, can be rewritten as

% S_i' = \frac{S_i}{\sum_{k=1}^K\sum_{j=1}^nz_{j(t-k)}\times \frac{1}{w_k}} % \qquad I_i' = \frac{I_i}{\sum_{k=1}^K\sum_{j=1}^nz_{j(t-k)} \times\frac{1}{w_k}}

For more details on these measurements, please refer to the vignette titled Time Discounted Infection and Susceptibility.

Value

A numeric column vector (matrix) of size n with either infection/susceptibility rates.

Discount rate

Discount rate, w_k in the formulas above, can be either exponential or linear. When expdiscount=TRUE, w_k = (1 + r)^{k-1}, otherwise it will be w_k = k.

Note that when K=1, the above formulas are equal to the ones presented in Valente et al. (2015).

Author(s)

George G. Vega Yon

References

Thomas W. Valente, Stephanie R. Dyal, Kar-Hai Chu, Heather Wipfli, Kayo Fujimoto Diffusion of innovations theory applied to global tobacco control treaty ratification, Social Science & Medicine, Volume 145, November 2015, Pages 89-97, ISSN 0277-9536 doi:10.1016/j.socscimed.2015.10.001

Myers, D. J. (2000). The Diffusion of Collective Violence: Infectiousness, Susceptibility, and Mass Media Networks. American Journal of Sociology, 106(1), 173–208. doi:10.1086/303110

See Also

The user can visualize the distribution of both statistics by using the function plot_infectsuscep

Other statistics: bass, classify_adopters(), cumulative_adopt_count(), dgr(), ego_variance(), exposure(), hazard_rate(), moran(), struct_equiv(), threshold(), vertex_covariate_dist()

Examples

# Creating a random dynamic graph
set.seed(943)
graph <- rgraph_er(n=100, t=10)
toa <- sample.int(10, 100, TRUE)

# Computing infection and susceptibility (K=1)
infection(graph, toa)
susceptibility(graph, toa)

# Now with K=4
infection(graph, toa, K=4)
susceptibility(graph, toa, K=4)

Find and remove isolated vertices

Description

Find and remove unconnected vertices from the graph.

Usage

isolated(
  graph,
  undirected = getOption("diffnet.undirected", FALSE),
  self = getOption("diffnet.self", FALSE)
)

drop_isolated(
  graph,
  undirected = getOption("diffnet.undirected", FALSE),
  self = getOption("diffnet.self", FALSE)
)

Arguments

Value

When graph is an adjacency matrix:

Otherwise, when graph is a list

Author(s)

George G. Vega Yon

See Also

Other data management functions: diffnet-class, edgelist_to_adjmat(), egonet_attrs(), survey_to_diffnet()

Examples

# Generating random graph
set.seed(123)
adjmat <- rgraph_er()

# Making nodes 1 and 4 isolated
adjmat[c(1,4),] <- 0
adjmat[,c(1,4)] <- 0
adjmat

# Finding isolated nodes
iso <- isolated(adjmat)
iso

# Removing isolated nodes
drop_isolated(adjmat)


# Now with a dynamic graph
graph <- rgraph_er(n=10, t=3)

# Making 1 and 5 isolated
graph <- lapply(graph, "[<-", i=c(1,5), j=1:10, value=0)
graph <- lapply(graph, "[<-", i=1:10, j=c(1,5), value=0)
graph

isolated(graph)
drop_isolated(graph)

Korean Family Planning

Description

From Valente (1995) “Scholars at Seoul National University's School of Public Health (Park, Chung, Han & Lee, 1974) collected data on the adoption of family planning methods among all married women of child-bearing age 25 in Korea villages in 1973 (N = 1,047).”

Format

A data frame with 1,047 rows and 432 columns:

village

Village of residence

id

Respondent ID number

recno1

Card number NA

studno1

Study number NA

area1

Village of residence

id1

Respondent ID number

nmage1

Number males age 0

nmage2

Number males age 0-4

nmage3

Number males age 5-9

nmage4

Number males age 10-14

nmage5

Number males age 15-19

nmage6

Number males age 20-24

nmage7

Number males age 25-29

nmage8

Number males age 30-34

nmage9

Number males age 35-39

nmage10

Number males age 40-44

nmage11

Number males age 45-49

nmage12

Number males age 50-54

nmage13

Number males age 55-59

nmage14

Number males age 60-64

nmage15

Number males age 65-69

nmage16

Number males age 70-74

nmage17

Number males age 75-79

nmage18

Number males age 80+

nfage1

Number females age 0

nfage2

Number females age 0-4

nfage3

Number females age 5-9

nfage4

Number females age 10-14

nfage5

Number females age 15-19

nfage6

Number females age 20-24

nfage7

Number females age 25-29

nfage8

Number females age 30-34

nfage9

Number females age 35-39

nfage10

Number females age 40-44

nfage11

Number females age 45-49

nfage12

Number females age 50-54

nfage13

Number females age 55-59

nfage14

Number females age 60-64

nfage15

Number females age 65-69

nfage16

Number females age 70-74

nfage17

Number females age 75-79

nfage18

Number females age 80+

pregs

total pregnancies

pregs1

number normal deliveries

pregs2

number of induced abortions

pregs3

number of spontaneous abortions

pregs4

number of still births

pregs5

number of deaths after live birth

pregs6

currently pregnant

sons

number of sons

daughts

number of daughters

planning

Ever heard of FP or birth control

loop1

Awareness of Loop

loop2

Detailed knowledge of Loop

loop3

Attitudes toward Loop

loop4

Knowledge of Loop used by neighbors

loop5

Knowledge of place of service for Loop

pill1

Awareness of Pill

pill2

Detailed knowledge of Pill

pill3

Attitudes toward Pill

pill4

Knowledge of Pill used by neighbors

pill5

Knowledge of place of service for Pill

vase1

Awareness of Vasectomy

vase2

Detailed knowledge of Vasectomy

vase3

Attitudes toward Vasectomy

vase4

Knowledge of Vasectomy used by neighbors

vase5

Knowledge of place of service for Vasectomy

cond1

Awareness of Condoms

cond2

Detailed knowledge Condoms

cond3

Attitudes toward Condoms

cond4

Knowledge of Condoms used by neighbors

cond5

Knowledge of place of service for Condoms

rhyt1

Awareness of Rhythm

rhyt2

Detailed knowledge Rhythm

rhyt3

Attitudes toward Rhythm

rhyt4

Knowledge of Rhythm used by neighbors

bbt1

Awareness of Basic Body Temperature

bbt2

Detailed knowledge Basic Body Temperature

bbt3

Attitudes toward BBT

recno2

Record Number NA

studno2

Study Number NA

area2

village number

id2

id number

bbt4

Knowledge of BBT used by neighbors

diap1

Awareness of Diaphragm

diap2

Detailed knowledge Diaphragm

diap3

Attitudes toward Diaphragm

diap4

Knowledge of Diaphragm used by neighbors

with1

Awareness of Withdrawal

with2

Detailed knowledge Withdrawal

with3

Attitudes toward Withdrawal

with4

Knowledge of Withdrawal used by neighbors

tuba1

Awareness of Tubal Ligation

tuba2

Detailed knowledge TL

tuba3

Attitudes toward TL

tuba4

Knowledge of TL used by neighbors

fp1

Experience with an FP practice

fp2

Reasons for not practicing

fp3

What would you do if problem was solved

fp4

Any other reason for not practicing

fp5

Reasons for practicing

fp6

time between decision and adoption

fp7

reasons for time lag

fp8

Ever discontinued practicing

fp9

Reasons for discontinuing

fp10

Attitude toward FP

child1

Ideal number of sons

child2

Ideal number of daughters

child3

Ideal number of children regardless of sex

child4

what do if kept having girls

comop1

Spousal communication on # of children

comop2

Spousal communication on FP

comop3

Consensus on opinion between couple

comop4

What was the difference

comop5

Opinion on who should practice

comop6

Different opinions on who should practice

comop7

Who should make final decision

comop8

Residence in old age

net11

Neighbors talk to about FP- 1

net12

Neighbors talk to about FP- 2

net13

Neighbors talk to about FP- 3

net14

Neighbors talk to about FP- 4

net15

Neighbors talk to about FP- 5

famawe1

Family members of FP Practice

famawe2

Parents awareness of FP Practice

famawe3

How did parents-in-law become aware

famawe4

How did parents become aware

famawe5

How did husband become aware

advic1

Advice given to neighbors where to go

advic2

Advice given on method

advic3

Ever met persons who give advice on FP

advic4

Credibility of person advising on FP

advic5

Counter advice given to others

rumor1

Rumors on Loop

rumor2

Rumors on Pill

rumor3

Rumors on Vasectomy

rumor4

Rumors on Condom

rumor5

Rumors on Tuballigation

media1

Possession of Radio

media2

Possession of TV

media3

Subscription to Newspaper

media4

Subscription to Happy Home

media5

Subscription to other magazine

media6

Radio exposure to FP

media7

TV exposure to FP

media8

Daily paper exposure to FP

media9

Happy Home exposure to FP

media10

Magazine exposure to FP

media11

Movie or slide exposure to FP

media12

Poster exposure to FP

media13

Pamphlet exposure to FP

media14

FP Meeting exposure to FP

recno3

Record number NA

studno3

Study number NA

area3

village

id3

id

media15

Public lecture exposure to FP

media16

Mobile van exposure to FP

media17

Neighbors exposure to FP

media18

Workers home visiting exposure to FP

media19

Husband exposure to FP

club1

Awareness of clubs in community

club2

Membership in club

club3

Reasons for not becoming a member

club4

Feeling of necessity of club

club5

Visit of mobile van to area

club6

Service received from van

club7

Decision-making on FP on # children

club8

Decision-making on important goods

club9

Decision-making on childrens discipline

club10

Decision making on purchase wife clothes

net21

Closest neighbor most frequently met

n1adv

Advice received from neighbor 1

n1prac

practice of FP by neighbor 1

net22

Closest neighbor person 2

n2adv

Advice received from neighbor 2

n2prac

Practice of FP by neighbor 2

net23

Closest neighbor person 3

n3adv

Advice received from neighbor 3

n3prac

Practice of FP by neighbor 3

net24

Closest neighbor 4

n4adv

Advice received from neighbor 4

n4prac

Practice of FP by neighbor 4

net25

Closest neighbor 5

n5adv

Advice received from neighbor 5

n5prac

Practice of FP by neighbor 5

stand

Standard living of above neighbors

educ

Education level of named neighbors

net31

Advice on FP sought from 1

net32

Advice on FP sought from 2

net33

Advice on FP sought from 3

net34

Advice on FP sought from 4

net35

Advice on FP sought from 5

net41

Information provided on FP by 1

net42

Information provided on FP by 1

net43

Information provided on FP by 1

net44

Information provided on FP by 1

net45

Information provided on FP by 1

net51

Seek advice on induced abortion 1

net52

Seek advice on induced abortion 2

net53

Seek advice on induced abortion 3

net54

Seek advice on induced abortion 4

net55

Seek advice on induced abortion 5

age

Age of respondent

agemar

Age at first marriage

recno4

Rec no NA

studno4

Study no NA

area4

village

id4

id

net61

Advice on health sought from 1

net62

Advice on health sought from 2

net63

Advice on health sought from 3

net64

Advice on health sought from 4

net65

Advice on health sought from 5

net71

Advice on purchase of goods 1

net72

Advice on purchase of goods 2

net73

Advice on purchase of goods 3

net74

Advice on purchase of goods 4

net75

Advice on purchase of goods 5

net81

Advice on childrens education 1

net82

Advice on childrens education 2

net83

Advice on childrens education 3

net84

Advice on childrens education 4

net85

Advice on childrens education 5

rfampl1

Advice on FP sought by 1

rfampl2

Advice on FP sought by 2

rfampl3

Advice on FP sought by 3

rfampl4

Advice on FP sought by 4

rfampl5

Advice on FP sought by 5

rfampll

Leadership score - indegree FP

rabort1

Advice on abortion sought by 1

rabort2

Advice on abortion sought by 2

rabort3

Advice on abortion sought by 3

rabort4

Advice on abortion sought by 4

rabort5

Advice on abortion sought by 5

rabortl

Leadership score - indegree abortion

rhealth1

Advice on health sought by 1

rhealth2

Advice on health sought by

rhealth3

Advice on health sought by

rhealth4

Advice on health sought by

rhealth5

Advice on health sought by

rhealthl

Leadership score - indegree health

recno5

rec no NA

studno5

study no NA

area5

village

id5

id

rgoods1

Advice on purchases sought by 1

rgoods2

Advice on purchases sought by 2

rgoods3

Advice on purchases sought by 3

rgoods4

Advice on purchases sought by 4

rgoods5

Advice on purchases sought by 5

rgoodsl

Leadership score - indegree purchases

reduc1

Advice on education sought by 1

reduc2

Advice on education sought by 2

reduc3

Advice on education sought by 3

reduc4

Advice on education sought by 4

reduc5

Advice on education sought by 5

reducl

Leadership score - indegree education

hub1

Husbands friend 1

hub2

Husbands friend 2

hub3

Husbands friend 3

hub4

Husbands friend 4

hub5

Husbands friend 5

hubed

Husbands education

wifeed

Wifes education

wiferel

Wifes religion

hubocc

Husbands occupation

wifeocc

Wifes occupation

know1

Can you insert a loop yourself

know2

Can you remove it alone

know3

Can a man use a loop

know4

How long can a loop be used

know5

Which doctor

know6

Doctor or nurse

know7

Oral pill method

know8

Can men take pills

know9

Long term use

know10

Time required for vasectomy

know11

Does vasectomy = castration

know12

Can any doctor do vasectomies

pref1

Who prefer use: Husband or wife

pref2

Reasons for preferring FP practice by wife

pref3

Reasons for preferring FP practice by husband

ageend

Ideal age to end childbearing

cfp

Current status of FP

cfatt1

Husbands attitude

cfatt2

In-laws attitude

cfatt3

Own parents attitude

cbyr

Start of period from year

cbmnth

Start of period from month

ceyr

End of period year

cemnth

End of period month

clngth

Length of period

cawe1

FP contact

cawe2

Awareness of contraceptive method at the time

cawe3

Awareness of service site

cawe4

Credibiilty

recno6

rec no NA

studno6

study no NA

area6

village

id6

id

fpt1

FP Status time 1

fatt1t1

Husbands attitude T1

fatt2t1

In-laws attitude T1

fatt3t1

Own parents attitude T1

byrt1

Start of Time 1 from year

lngtht1

Length of Time 1

awe1t1

FP Contact Time 1

awe2t1

Methods known at Time 1

awe3t1

Knowledge of service sites Time 1

awe4t1

Credibility of service site Time 1

fpt2

FP Status time 2

fatt1t2

Husbands attitude T2

fatt2t2

In-laws attitude T2

fatt3t2

Own parents attitude T2

byrt2

Start of Time 2 from year

lngtht2

Length of Time 2

awe1t2

FP Contact Time 2

awe2t2

Methods known at Time 2

awe3t2

Knowledge of service sites Time 2

awe4t2

Credibility of service site Time 2

fpt3

FP Status time 3

fatt1t3

Husbands attitude T3

fatt2t3

In-laws attitude T3

fatt3t3

Own parents attitude T3

byrt3

Start of Time 3 from year

lngtht3

Length of Time 3

awe1t3

FP Contact Time 3

awe2t3

Methods known at Time 3

awe3t3

Knowledge of service sites Time 3

awe4t3

Credibility of service site Time 3

fpt4

FP Status time 4

fatt1t4

Husbands attitude T4

fatt2t4

In-laws attitude T4

fatt3t4

Own parents attitude T4

byrt4

Start of Time 4 from year

lngtht4

Length of Time 4

awe1t4

FP Contact Time 4

awe2t4

Methods known at Time 4

awe3t4

Knowledge of service sites Time 4

awe4t4

Credibility of service site Time 4

fpt5

FP Status time 5

fatt1t5

Husbands attitude T5

fatt2t5

In-laws attitude T5

fatt3t5

Own parents attitude T5

byrt5

Start of Time 5 from year

lngtht5

Length of Time 5

awe1t5

FP Contact Time 5

awe2t5

Methods known at Time 5

awe3t5

Knowledge of service sites Time 5

awe4t5

Credibility of service site Time 5

fpt6

FP Status time 6

fatt1t6

Husbands attitude T6

fatt2t6

In-laws attitude T6

fatt3t6

Own parents attitude T6

byrt6

Start of Time 6 from year

lngtht6

Length of Time 6

awe1t6

FP Contact Time 6

awe2t6

Methods known at Time 6

awe3t6

Knowledge of service sites Time 6

awe4t6

Credibility of service site Time 6

recno7

rec no NA

studno7

study no NA

area7

village

id7

id

fpt7

FP Status time 7

fatt1t7

Husbands attitude T7

fatt2t7

In-laws attitude T7

fatt3t7

Own parents attitude T7

byrt7

Start of Time 7 from year

lngtht7

Length of Time 7

awe1t7

FP Contact Time 7

awe2t7

Methods known at Time 7

awe3t7

Knowledge of service sites Time 7

awe4t7

Credibility of service site Time 7

fpt8

FP Status time 8

fatt1t8

Husbands attitude T8

fatt2t8

In-laws attitude T8

fatt3t8

Own parents attitude T8

byrt8

Start of Time 8 from year

lngtht8

Length of Time 8

awe1t8

FP Contact Time 8

awe2t8

Methods known at Time 8

awe3t8

Knowledge of service sites Time 8

awe4t8

Credibility of service site Time 8

fpt9

FP Status time 9

fatt1t9

Husbands attitude T9

fatt2t9

In-laws attitude T9

fatt3t9

Own parents attitude T9

byrt9

Start of Time 9 from year

lngtht9

Length of Time 9

awe1t9

FP Contact Time 9

awe2t9

Methods known at Time 9

awe3t9

Knowledge of service sites Time 9

awe4t9

Credibility of service site Time 9

fpt10

FP Status time 10

fatt1t10

Husbands attitude T10

fatt2t10

In-laws attitude T10

fatt3t10

Own parents attitude T10

byrt10

Start of Time 10 from year

lngtht10

Length of Time 10

awe1t10

FP Contact Time 10

awe2t10

Methods known at Time 10

awe3t10

Knowledge of service sites Time 10

awe4t10

Credibility of service site Time 10

fpt11

FP Status time 11

fatt1t11

Husbands attitude T11

fatt2t11

In-laws attitude T11

fatt3t11

Own parents attitude T11

byrt11

Start of Time 11 from year

lngtht11

Length of Time 11

awe1t11

FP Contact Time 11

awe2t11

Methods known at Time 11

awe3t11

Knowledge of service sites Time 11

awe4t11

Credibility of service site Time 11

fpt12

FP Status time 12

fatt1t12

Husbands attitude T12

fatt2t12

In-laws attitude T12

fatt3t12

Own parents attitude T12

byrt12

Start of Time 12 from year

lngtht12

Length of Time 12

awe1t12

FP Contact Time 12

awe2t12

Methods known at Time 12

awe3t12

Knowledge of service sites Time 12

awe4t12

Credibility of service site Time 12

ado

adopt times years converted to 1=63

ado1

ado2

ado3

commun

Village number

toa

Time of Adoption

study

Study (for when multiple diff studies used)

Details

The dataset has 1,047 respondents (women) from 25 communities. Collected during 1973 it spans 11 years of data.

Source

The Korean Family Planning data were stored on a Vax tape that Rogers had given to Marc Granovetter who then gave it to his colleague Roland Soong (see Granovetter & Soong, 1983). Granovetter instructed Song to send the tape to me and I had it loaded on the Vax machine at USC in 1990 and was able to download the data to a PC. The first two datasets were acquired for my dissertation (Valente, 1991) and the third added as I completed my book on Network Models of the Diffusion of Innovations (Valente, 1995; also see Valente, 2005).

References

Everett M. Rogers, & Kincaid, D. L. (1981). Communication Networks: Toward a New Paradigm for Research. (C. Macmillan, Ed.). New York; London: Free Press.

Valente, T. W. (1995). Network models of the diffusion of innovations (2nd ed.). Cresskill N.J.: Hampton Press.

See Also

Other diffusion datasets: brfarmers, brfarmersDiffNet, diffusion-data, fakeDynEdgelist, fakeEdgelist, fakesurvey, fakesurveyDyn, kfamilyDiffNet, medInnovations, medInnovationsDiffNet

diffnet version of the Korean Family Planning data

Description

A directed dynamic graph with 1,047 vertices and 11 time periods. The attributes in the graph are static and described in kfamily.

Format

A diffnet class object.

See Also

Other diffusion datasets: brfarmers, brfarmersDiffNet, diffusion-data, fakeDynEdgelist, fakeEdgelist, fakesurvey, fakesurveyDyn, kfamily, medInnovations, medInnovationsDiffNet

Non-zero element-wise comparison between two sparse matrices

Description

Taking advantage of matrix sparseness, the function only evaluates fun between pairs of elements of A and B where either A or B have non-zero values. This can be helpful to implement other binary operators between sparse matrices that may not be implemented in the Matrix package.

Usage

matrix_compare(A, B, fun)

compare_matrix(A, B, fun)

Arguments

Details

Instead of comparing element by element, the function loops through each matrix non-zero elements to make the comparisons, which in the case of sparse matrices can be more efficient (faster). Algorithmically it can be described as follows:

# Matrix initialization
init ans[n,m];

# Looping through non-zero elements of A
for e_A in E_A:
  ans[e_A] = fun(A[e_A], B[e_A])

# Looping through non-zero elements of B and applying the function
# in e_B only if it was not applied while looping in E_A.
for e_B in E_B:
  if (ans[e_B] == Empty)
    ans[e_B] = fun(A[e_B], B[e_B])

compare_matrix is just an alias for matrix_compare.

Value

An object of class dgCMatrix of size n*m.

See Also

Other dyadic-level comparison functions: vertex_covariate_compare(), vertex_covariate_dist()

Examples

# These two should yield the same results -----------------------------------

# Creating two random matrices
set.seed(89)
A <- rgraph_ba(t = 9, m = 4)
B <- rgraph_ba(t = 9, m = 4)
A;B

# Comparing
ans0 <- matrix_compare(A,B, function(a,b) (a+b)/2)

ans1 <- matrix(0, ncol=10, nrow=10)
for (i in 1:10)
  for (j in 1:10)
    ans1[i,j] <- mean(c(A[i,j], B[i,j]))

# Are these equal?
all(ans0[] == ans1[]) # Should yield TRUE

Medical Innovation

Description

From Valente (1995) “Coleman, Katz and Menzel from Columbia University's Bureau of Applied Research studied the adoption of tetracycline by physiciams in four Illinois communities in 1954.[...] Tetracycline was a powerful and useful antibiotic just introduced in the mid-1950s”

Format

A data frame with 125 rows and 59 columns:

city

city id

id

sequential respondent id

detail

detail man

meet

meetings, lectures, hospitals

coll

colleagues

attend

attend professional meets

proage

professional age

length

lenght of reside in community

here

only practice here

science

science versus patients

position

position in home base

journ2

journal subscriptions

paadico

Percent alter adoption date imp

ado

adoption month 1 to 18

thresh

threshold

ctl

corrected tl tl-exp level

catbak

category 1-init 2-marg 3-low tl

sourinfo

source of information

origid

original respondent id

adopt

adoption date 1= 11/53

recon

reconstructed med innov

date

date became aware

info

information source

most

most important info source

journ

journals

drug

drug houses

net1_1

advisor nomination1

net1_2

advisor nomination2

net1_3

advisor nomination3

net2_1

discuss nomination1

net2_2

discuss nomination2

net2_3

discuss nomination3

net3_1

friends nomination1

net3_2

friends nomination2

net3_3

friends nomination3

nojourn

number of pro journals receive

free

free time companions

social

med discussions during social

club

club membership

friends

friends are doctors

young

young patients

nonpoor

nonpoverty patients

office

office visits

house

house calls

tend

tendency to prescribe drugs

reltend

relative tendency to prescribe

perc

perceived drug competition

proximty

physical proximity to other doc

home

home base hospital affiliation

special

specialty

belief

belief in science

proage2

profesional age 2

presc

prescription prone

detail2

contact with detail man

dichot

dichotomous personal preference

expect

adoption month expected

recall

recalls adopting

commun

Number of community

toa

Time of Adoption

study

Number of study in Valente (1995)

Details

The collected dataset has 125 respondents (doctors), and spans 17 months of data collected in 1955. Time of adoption of non-adopters has been set to month 18 (see the manual entry titled Difussion Network Datasets).

Source

The Medical Innovation data were stored in file cabinets in a basement building at Columbia University. Ron Burt (1987) acquired an NSF grant to develop network diffusion models and retrieve the original surveys and enter them into a database. He distributed copies of the data on diskette and sent one to me, Tom Valente, and I imported onto a PC environment.

References

Coleman, J., Katz, E., & Menzel, H. (1966). Medical innovation: A diffusion study (2nd ed.). New York: Bobbs-Merrill

Valente, T. W. (1995). Network models of the diffusion of innovations (2nd ed.). Cresskill N.J.: Hampton Press.

See Also

Other diffusion datasets: brfarmers, brfarmersDiffNet, diffusion-data, fakeDynEdgelist, fakeEdgelist, fakesurvey, fakesurveyDyn, kfamily, kfamilyDiffNet, medInnovationsDiffNet

diffnet version of the Medical Innovation data

Description

A directed dynamic graph with 125 vertices and 18 time periods. The attributes in the graph are static and described in medInnovations.

Format

A diffnet class object.

See Also

Other diffusion datasets: brfarmers, brfarmersDiffNet, diffusion-data, fakeDynEdgelist, fakeEdgelist, fakesurvey, fakesurveyDyn, kfamily, kfamilyDiffNet, medInnovations

Optimal Leader/Mentor Matching

Description

Implementes the algorithm described in Valente and Davis (1999)

Usage

mentor_matching(
  graph,
  n,
  cmode = "indegree",
  lead.ties.method = "average",
  geodist.args = list()
)

leader_matching(
  graph,
  n,
  cmode = "indegree",
  lead.ties.method = "average",
  geodist.args = list()
)

## S3 method for class 'diffnet_mentor'
plot(
  x,
  y = NULL,
  vertex.size = "degree",
  minmax.relative.size = getOption("diffnet.minmax.relative.size", c(0.01, 0.04)),
  lead.cols = grDevices::topo.colors(attr(x, "nleaders")),
  vshapes = c(Leader = "square", Follower = "circle"),
  add.legend = TRUE,
  main = "Mentoring Network",
  ...
)

Arguments

Details

The algorithm works as follows:

1. Find the top n individuals ranking them by dgr(graph, cmode). The rank is computed by the function rank. Denote this set M.

2. Compute the geodesic matrix.

3. For each v in V do:

   1. Find the mentor m in M such that is closest to v

   2. Were there a tie, choose the mentor that minimizes the average path length from v's direct neighbors to m.

   3. If there are no paths to any member of M, or all have the same average path length to v's neighbors, then assign one randomly.

Plotting is done via the function plot.igraph.

When vertex.size is either of "degree", "indegree", or "outdegree", vertex.size will be replace with dgr(.,cmode = ) so that the vertex size reflects the desired degree.

The argument minmax.relative.size is passed to rescale_vertex_igraph which adjusts vertex.size so that the largest and smallest vertices have a relative size of minmax.relative.size[2] and minmax.relative.size[1] respectively with respect to the x-axis.

Value

An object of class diffnet_mentor and data.frame with the following columns:

The object also contains the following attributes:

.

References

Valente, T. W., & Davis, R. L. (1999). Accelerating the Diffusion of Innovations Using Opinion Leaders. The ANNALS of the American Academy of Political and Social Science, 566(1), 55–67. doi:10.1177/000271629956600105

Examples

# A simple example ----------------------------------------------------------
set.seed(1231)
graph <- rgraph_ws(n=50, k = 4, p = .5)

# Looking for 3 mentors
ans <- mentor_matching(graph, n = 3)

head(ans)
table(ans$match) # We actually got 9 b/c of ties

# Visualizing the mentor network
plot(ans)

Computes Moran's I correlation index

Description

Natively built for computing Moran's I on dgCMatrix objects, this routine allows computing the I on large sparse matrices (graphs). Part of its implementation was based on ape::Moran.I, which computes the I for dense matrices.

Usage

moran(x, w, normalize.w = TRUE, alternative = "two.sided")

Arguments

Details

In the case that the vector x is close to constant (degenerate random variable), the statistic becomes irrelevant, and furthermore, the standard error tends to be undefined (NaN).

Value

A list of class diffnet_moran with the following elements:

Author(s)

George G. Vega Yon

References

Moran's I. (2015, September 3). In Wikipedia, The Free Encyclopedia. Retrieved 06:23, December 22, 2015, from https://en.wikipedia.org/w/index.php?title=Moran%27s_I&oldid=679297766

See Also

Other statistics: bass, classify_adopters(), cumulative_adopt_count(), dgr(), ego_variance(), exposure(), hazard_rate(), infection(), struct_equiv(), threshold(), vertex_covariate_dist()

Other Functions for inference: bootnet(), struct_test()

Examples

if (require("ape")) {

  # Generating a small random graph
  set.seed(123)
  graph <- rgraph_ba(t = 4)
  w <- approx_geodesic(graph)
  x <- rnorm(5)

  # Computing Moran's I
  moran(x, w)

  # Comparing with the ape's package version
  ape::Moran.I(x, as.matrix(w))

}

Network data formats

Description

List of accepted graph formats

Details

The netdiffuseR package can handle different types of graph objects. Two general classes are defined across the package's functions: static graphs, and dynamic graphs.

• In the case of static graphs, these are represented as adjacency matrices of size n\times n and can be either matrix (dense matrices) or dgCMatrix (sparse matrix from the Matrix package). While most of the package functions are defined for both classes, the default output graph is sparse, i.e. dgCMatrix.

• With respect to dynamic graphs, these are represented by either a diffnet object, an array of size n\times n \times T, or a list of size T with sparse matrices (class dgCMatrix) of size n\times n. Just like the static graph case, while most of the functions accept both graph types, the default output is dgCMatrix.

Value

No return value (this manual entry only provides information).

diffnet objects

In the case of diffnet-class objects, the following arguments can be omitted when calling fuictions suitable for graph objects:

• toa: Time of Adoption vector

• adopt: Adoption Matrix

• cumadopt: Cumulative Adoption Matrix

• undirected: Whether the graph is directed or not

Objects' names

When possible, netdiffuseR will try to reuse graphs dimensional names, this is, rownames, colnames, dimnames and names (in the case of dynamic graphs as lists). Otherwise, when no names are provided, these will be created from scratch.

Author(s)

George G. Vega Yon

netdiffuseR default options

Description

netdiffuseR default options

Details

Set of default options used by the package. These can be retrieved via getOption using the prefix diffnet (see examples)

Value

The full list of options follows:

Author(s)

George G. Vega Yon

Examples

getOption("diffnet.undirected")
getOption("diffnet.multiple")
getOption("diffnet.self")

Matching Estimators with Network Data

Description

WARNING: This function is still in development and has not been tested throughly. Following Aral et al. (2009), netmatch computes matching estimators for network data. The function netmatch_prepare, which prepares the data to be used with matchit from the MatchIt package, is called by netmatch.

Usage

netmatch_prepare(
  dat,
  graph,
  timevar,
  depvar,
  covariates,
  treat_thr = rep(1L, length(graph)),
  adopt_thr = rep(1L, length(graph)),
  expo_pcent = FALSE,
  expo_lag = 0L
)

netmatch(
  dat,
  graph,
  timevar,
  depvar,
  covariates,
  treat_thr = rep(1L, length(graph)),
  adopt_thr = rep(1L, length(graph)),
  expo_pcent = FALSE,
  expo_lag = 0L,
  ...
)

Arguments

Details

In Aral et al. (2009), the matching estimator is used as a response to the fact that the observed network is homophilous. Essentially, using exposure as a treatment indicator, which is known to be endogenous, we can apply the same principle of matching estimators in which, after controlling for characteristics (covariates), individuals from the treated group (exposed to some behavior) can be compared to individuals from the control group (not exposed to that behavior), as the only difference between the two is the exposure.

As pointed out in King & Nielsen (2015), it is suggested that, contrary to what Aral et al. (2009), the matching is not performed over propensity score since it is know that the later can increase imbalances in the data and thus obtaining exactly the opposed outcome that matching based estimators pursue.

A couple of good references for matching estimators are Imbens and Wooldridge (2009), and Sekhon (2008).

Value

In the case of netmatch_prepare

netmatch returns the following:

Author(s)

George G. Vega Yon

References

Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences of the United States of America, 106(51), 21544–21549. doi:10.1073/pnas.0908800106

Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5–86. doi:10.1257/jel.47.1.5

King, G., & Nielsen, R. (2015). Why Propensity Scores Should Not Be Used for.

Sekhon, J. S. (2008). The Neyman-Rubin Model of Causal Inference and Estimation Via Matching Methods. The Oxford Handbook of Political Methodology. doi:10.1093/oxfordhb/9780199286546.003.0011

Coercion between diffnet, network and networkDynamic

Description

Coercion between diffnet, network and networkDynamic

Usage

diffnet_to_network(graph, slices = 1:nslices(graph), ...)

diffnet_to_networkDynamic(
  graph,
  slices = 1:nslices(graph),
  diffnet2net.args = list(),
  netdyn.args = list()
)

networkDynamic_to_diffnet(graph, toavar)

network_to_diffnet(
  graph = NULL,
  graph.list = NULL,
  toavar,
  t0 = NULL,
  t1 = NULL
)

Arguments

Details

diffnet_to_networkDynamic calls diffnet_to_network and uses the output to call networkDynamic, passing the resulting list of network objects as network.list (see networkDynamic).

By default, diffnet_to_networkDynamic passes net.obs.period as

  net.obs.period = list(
    observations = list(range(graph$meta$pers)),
    mode="discrete",
    time.increment = 1,
    time.unit = "step"
  )

By default, networkDynamic_to_diffnet uses the first slice as reference for vertex attributes and times of adoption.

By default, network_to_diffnet uses the first element of graph (a list) as reference for vertex attributes and times of adoption.

Value

diffnet_to_network returns a list of length length(slices) in which each element is a network object corresponding a slice of the graph (diffnet object). The attributes list will include toa (time of adoption).

An object of class networkDynamic.

Caveats

Since diffnet does not support edges attributes, these will be lost when converting from network-type objects. The same applies to network attributes.

See Also

Other Foreign: igraph, read_pajek(), read_ucinet_head()

Examples

# Cohersing a diffnet to a list of networks ---------------------------------
set.seed(1)
ans <- diffnet_to_network(rdiffnet(20, 2))
ans

# and back
network_to_diffnet(graph.list = ans, toavar="toa")

# If it was static, we can use -graph- instead
network_to_diffnet(ans[[1]], toavar="toa")

# A random diffusion network ------------------------------------------------
set.seed(87)
dn  <- rdiffnet(50, 4)
ans <- diffnet_to_networkDynamic(dn)

# and back
networkDynamic_to_diffnet(ans, toavar = "toa")

Count the number of vertices/edges/slices in a graph

Description

Count the number of vertices/edges/slices in a graph

Usage

nvertices(graph)

nnodes(graph)

nedges(graph)

nlinks(graph)

nslices(graph)

Arguments

Details

nnodes and nlinks are just aliases for nvertices and nedges respectively.

Value

For nvertices and nslices, an integer scalar equal to the number of vertices and slices in the graph. Otherwise, from nedges, either a list of size t with the counts of edges (non-zero elements in the adjacency matrices) at each time period, or, when graph is static, a single scalar with such number.

Examples

# Creating a dynamic graph (we will use this for all the classes) -----------
set.seed(13133)
diffnet <- rdiffnet(100, 4)

# Lets use the first time period as a static graph
graph_mat <- diffnet$graph[[1]]
graph_dgCMatrix <- methods::as(graph_mat, "dgCMatrix")

# Now lets generate the other dynamic graphs
graph_list  <- diffnet$graph
graph_array <- as.array(diffnet) # using the as.array method for diffnet objects

# Now we can compare vertices counts
nvertices(diffnet)
nvertices(graph_list)
nvertices(graph_array)

nvertices(graph_mat)
nvertices(graph_dgCMatrix)

# ... and edges count
nedges(diffnet)
nedges(graph_list)
nedges(graph_array)

nedges(graph_mat)
nedges(graph_dgCMatrix)

Permute the values of a matrix

Description

permute_graph Shuffles the values of a matrix either considering loops and multiple links (which are processed as cell values different than 1/0). rewire_qap generates a new graph graph' that is isomorphic to graph.

Usage

permute_graph(graph, self = FALSE, multiple = FALSE)

rewire_permute(graph, self = FALSE, multiple = FALSE)

rewire_qap(graph)

Arguments

Value

A permuted version of graph.

Author(s)

George G. Vega Yon

References

Anderson, B. S., Butts, C., & Carley, K. (1999). The interaction of size and density with graph-level indices. Social Networks, 21(3), 239–267. doi:10.1016/S0378-8733(99)00011-8

Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research, 27(2), 209–20.

See Also

This function can be used as null distribution in struct_test

Other simulation functions: rdiffnet(), rewire_graph(), rgraph_ba(), rgraph_er(), rgraph_ws(), ring_lattice()

Examples

# Simple example ------------------------------------------------------------
set.seed(1231)
g <- rgraph_ba(t=9)
g

# These preserve the density
permute_graph(g)
permute_graph(g)

# These are isomorphic to g
rewire_qap(g)
rewire_qap(g)

S3 plotting method for diffnet objects.

Description

S3 plotting method for diffnet objects.

Usage

## S3 method for class 'diffnet'
plot(
  x,
  y = NULL,
  t = 1,
  vertex.color = c(adopt = "steelblue", noadopt = "white"),
  vertex.size = "degree",
  main = "Diffusion network in time %d",
  minmax.relative.size = getOption("diffnet.minmax.relative.size", c(0.01, 0.04)),
  ...
)

Arguments

Details

Plotting is done via the function plot.igraph.

When vertex.size is either of "degree", "indegree", or "outdegree", vertex.size will be replace with dgr(.,cmode = ) so that the vertex size reflects the desired degree.

The argument minmax.relative.size is passed to rescale_vertex_igraph which adjusts vertex.size so that the largest and smallest vertices have a relative size of minmax.relative.size[2] and minmax.relative.size[1] respectively with respect to the x-axis.

Value

A matrix with the coordinates of the vertices.

Author(s)

George G. Vega Yon

See Also

Other diffnet methods: %*%(), as.array.diffnet(), c.diffnet(), diffnet-arithmetic, diffnet-class, diffnet_index, summary.diffnet()

Examples

data(medInnovationsDiffNet)
plot(medInnovationsDiffNet)

Visualize adopters and cumulative adopters

Description

Visualize adopters and cumulative adopters

Usage

plot_adopters(
  obj,
  freq = FALSE,
  what = c("adopt", "cumadopt"),
  add = FALSE,
  include.legend = TRUE,
  include.grid = TRUE,
  pch = c(21, 24),
  type = c("b", "b"),
  ylim = if (!freq) c(0, 1) else NULL,
  lty = c(1, 1),
  col = c("black", "black"),
  bg = c("tomato", "gray"),
  xlab = "Time",
  ylab = ifelse(freq, "Frequency", "Proportion"),
  main = "Adopters and Cumulative Adopters",
  ...
)

Arguments

Value

A matrix as described in cumulative_adopt_count.

Author(s)

George G. Vega Yon

See Also

Other visualizations: dgr(), diffusionMap(), drawColorKey(), grid_distribution(), hazard_rate(), plot_diffnet(), plot_diffnet2(), plot_infectsuscep(), plot_threshold(), rescale_vertex_igraph()

Examples

# Generating a random diffnet -----------------------------------------------
set.seed(821)
diffnet <- rdiffnet(100, 5, seed.graph="small-world", seed.nodes="central")

plot_adopters(diffnet)

# Alternatively, we can use a TOA Matrix
toa <- sample(c(NA, 2010L,2015L), 20, TRUE)
mat <- toa_mat(toa)
plot_adopters(mat$cumadopt)

Plot the diffusion process

Description

Creates a colored network plot showing the structure of the graph through time (one network plot for each time period) and the set of adopter and non-adopters in the network.

Usage

plot_diffnet(...)

## S3 method for class 'diffnet'
plot_diffnet(graph, ...)

## Default S3 method:
plot_diffnet(
  graph,
  cumadopt,
  slices = NULL,
  vertex.color = c("white", "tomato", "steelblue"),
  vertex.shape = c("square", "circle", "circle"),
  vertex.size = "degree",
  mfrow.par = NULL,
  main = c("Network in period %s", "Diffusion Network"),
  legend.args = list(),
  minmax.relative.size = getOption("diffnet.minmax.relative.size", c(0.01, 0.04)),
  background = NULL,
  ...
)

Arguments

Details

Plotting is done via the function plot.igraph.

When vertex.size is either of "degree", "indegree", or "outdegree", vertex.size will be replace with dgr(.,cmode = ) so that the vertex size reflects the desired degree.

The argument minmax.relative.size is passed to rescale_vertex_igraph which adjusts vertex.size so that the largest and smallest vertices have a relative size of minmax.relative.size[2] and minmax.relative.size[1] respectively with respect to the x-axis.

Plotting is done via the function plot.igraph.

In order to center the attention on the diffusion process itself, the positions of each vertex are computed only once by aggregating the networks through time, this is, instead of computing the layout for each time t, the function creates a new graph accumulating links through time.

The mfrow.par sets how to arrange the plots on the device. If T=5 and mfrow.par=c(2,3), the first three networks will be in the top of the device and the last two in the bottom.

The argument vertex.color contains the colors of non-adopters, new-adopters, and adopters respectively. The new adopters (default color "tomato") have a different color that the adopters when the graph is at their time of adoption, hence, when the graph been plotted is in t=2 and toa=2 the vertex will be plotted in red.

legend.args has the following default parameter:

Value

Calculated coordinates for the grouped graph (invisible).

Author(s)

George G. Vega Yon

See Also

Other visualizations: dgr(), diffusionMap(), drawColorKey(), grid_distribution(), hazard_rate(), plot_adopters(), plot_diffnet2(), plot_infectsuscep(), plot_threshold(), rescale_vertex_igraph()

Examples

# Generating a random graph
set.seed(1234)
n <- 6
nper <- 5
graph <- rgraph_er(n,nper, p=.3, undirected = FALSE)
toa <- sample(2000:(2000+nper-1), n, TRUE)
adopt <- toa_mat(toa)

plot_diffnet(graph, adopt$cumadopt)

Another way of visualizing diffusion

Description

Another way of visualizing diffusion

Usage

plot_diffnet2(graph, ...)

## S3 method for class 'diffnet'
plot_diffnet2(graph, toa, slice = nslices(graph), ...)

## Default S3 method:
plot_diffnet2(
  graph,
  toa,
  pers = min(toa, na.rm = TRUE):max(toa, na.rm = TRUE),
  color.ramp = grDevices::colorRamp(viridisLite::magma(20)),
  layout = NULL,
  key.width = 0.1,
  key.args = list(),
  main = "Diffusion dynamics",
  add.map = NULL,
  diffmap.args = list(kde2d.args = list(n = 100)),
  diffmap.alpha = 0.5,
  include.white = "first",
  vertex.size = "degree",
  minmax.relative.size = getOption("diffnet.minmax.relative.size", c(0.01, 0.04)),
  no.graph = FALSE,
  ...
)

Arguments

Details

Plotting is done via the function plot.igraph.

When vertex.size is either of "degree", "indegree", or "outdegree", vertex.size will be replace with dgr(.,cmode = ) so that the vertex size reflects the desired degree.

The argument minmax.relative.size is passed to rescale_vertex_igraph which adjusts vertex.size so that the largest and smallest vertices have a relative size of minmax.relative.size[2] and minmax.relative.size[1] respectively with respect to the x-axis.

If key.width<=0 then no key is created.

By defult, the function passes the following values to plot.igraph:

• vertex.label equals to ""

• vertex.frame.color equals to "white"

• add equals to TRUE

• rescale equals to FALSE

• vertex.size equals to rescale.fun(vertex.size)

Value

A list with the following elements

Author(s)

George G. Vega Yon

See Also

Other visualizations: dgr(), diffusionMap(), drawColorKey(), grid_distribution(), hazard_rate(), plot_adopters(), plot_diffnet(), plot_infectsuscep(), plot_threshold(), rescale_vertex_igraph()

Plot distribution of infect/suscep

Description

After calculating infectiousness and susceptibility of each individual on the network, it creates an nlevels by nlevels matrix indicating the number of individuals that lie within each cell, and draws a heatmap.

Usage

plot_infectsuscep(
  graph,
  toa,
  t0 = NULL,
  normalize = TRUE,
  K = 1L,
  r = 0.5,
  expdiscount = FALSE,
  bins = 20,
  nlevels = round(bins/2),
  h = NULL,
  logscale = TRUE,
  main = "Distribution of Infectiousness and\nSusceptibility",
  xlab = "Infectiousness of ego",
  ylab = "Susceptibility of ego",
  sub = ifelse(logscale, "(in log-scale)", NA),
  color.palette = function(n) viridisLite::viridis(n),
  include.grid = TRUE,
  exclude.zeros = FALSE,
  valued = getOption("diffnet.valued", FALSE),
  ...
)

Arguments

Details

This plotting function was inspired by Aral, S., & Walker, D. (2012).

By default the function will try to apply a kernel smooth function via kde2d. If not possible (because not enought data points), then the user should try changing the parameter h or set it equal to zero.

toa is passed to infection/susceptibility.

Value

A list with three elements:

Author(s)

George G. Vega Yon

References

Aral, S., & Walker, D. (2012). "Identifying Influential and Susceptible Members of Social Networks". Science, 337(6092), 337–341. doi:10.1126/science.1215842

See Also

Infectiousness and susceptibility are computed via infection and susceptibility.

Other visualizations: dgr(), diffusionMap(), drawColorKey(), grid_distribution(), hazard_rate(), plot_adopters(), plot_diffnet(), plot_diffnet2(), plot_threshold(), rescale_vertex_igraph()

Examples

# Generating a random graph -------------------------------------------------
set.seed(1234)
n <- 100
nper <- 20
graph <- rgraph_er(n,nper, p=.2, undirected = FALSE)
toa <- sample(1:(1+nper-1), n, TRUE)

# Visualizing distribution of suscep/infect
out <- plot_infectsuscep(graph, toa, K=3, logscale = FALSE)

Threshold levels through time

Description

Draws a graph where the coordinates are given by time of adoption, x-axis, and threshold level, y-axis.

Usage

plot_threshold(graph, expo, ...)

## S3 method for class 'diffnet'
plot_threshold(graph, expo, ...)

## S3 method for class 'array'
plot_threshold(graph, expo, ...)

## Default S3 method:
plot_threshold(
  graph,
  expo,
  toa,
  include_censored = FALSE,
  t0 = min(toa, na.rm = TRUE),
  attrs = NULL,
  undirected = getOption("diffnet.undirected"),
  no.contemporary = TRUE,
  main = "Time of Adoption by\nNetwork Threshold",
  xlab = "Time",
  ylab = "Threshold",
  vertex.size = "degree",
  vertex.color = NULL,
  vertex.label = "",
  vertex.label.pos = NULL,
  vertex.label.cex = 1,
  vertex.label.adj = c(0.5, 0.5),
  vertex.label.color = NULL,
  vertex.sides = 40L,
  vertex.rot = 0,
  edge.width = 2,
  edge.color = NULL,
  arrow.width = NULL,
  arrow.length = NULL,
  arrow.color = NULL,
  include.grid = FALSE,
  vertex.frame.color = NULL,
  bty = "n",
  jitter.factor = c(1, 1),
  jitter.amount = c(0.25, 0.025),
  xlim = NULL,
  ylim = NULL,
  edge.curved = NULL,
  background = NULL,
  ...
)

Arguments