netdiffuseR: Analysis of Diffusion and Contagion Processes on Networks

This package contains functions useful for analyzing network data for diffusion of innovations applications.

The package was developed as part of the paper 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 (available here).

From the 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); Valente (1995), Myers (2000), Iyengar and others (2011), Burt (1987); among others.

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

r citation(package="netdiffuseR")

To cite netdiffuseR in publications use the following paper:

  Valente TW, Vega Yon GG. Diffusion/Contagion Processes on Social
  Networks. Health Education & Behavior. 2020;47(2):235-248.
  doi:10.1177/1090198120901497

And the actual R package:

  Vega Yon G, Olivera Morales A, Valente T (2025). _netdiffuseR:
  Analysis of Diffusion and Contagion Processes on Networks_.
  doi:10.5281/zenodo.1039317 <https://doi.org/10.5281/zenodo.1039317>,
  R package version 1.23.0, <https://github.com/USCCANA/netdiffuseR>.

To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.

News

Changelog can be view here.

Installation

CRAN version

To get the CRAN (stable) version of the package, simple type

r install.packages("netdiffuseR")

Bleeding edge version

If you want the latest (unstable) version of netdiffuseR, using the remotes package, you can install netdiffuseR dev version as follows

r remotes::install_github('USCCANA/netdiffuseR', build_vignettes = TRUE)

You can skip building vignettes by setting build_vignettes = FALSE (so it is not required).

For the case of OSX users, there seems to be a problem when installing packages depending on Rcpp. This issue, developed here, can be solved by open the terminal and typing the following

sh curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2 sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /

before installing the package through devtools.

Binary versions

For the case of windows and mac users, they can find binary versions of the package here, netdiffuseR1…zip, and netdiffuseR1…tgz respectively. They can install this directly as follows (using the 1.16.3.29 version):

1. Install dependencies from CRAN r > install.packages(c("igraph", "Matrix", "SparseM", "RcppArmadillo", "sna"), dependencies=TRUE)

2. Download the binary version and install it as follows:

   ``` r

   install.packages("netdiffuseR_1.16.3.29.zip", repos=NULL) ```

   For windows users, and for Mac users:

   ``` r

   install.packages("netdiffuseR_1.16.3.29.tgz", repos=NULL) ```

Tutorials

Since starting netdiffuseR, we have done a couple of workshops at Sunbelt and NASN. Past and current workshops can be found at https://github.com/USCCANA/netdiffuser-workshop

Presentations

• ic2s2 2016 Evanston, IL: https://github.com/USCCANA/netdiffuser-ic2s22016 (poster)
• useR! 2016 Stanford, CA: https://github.com/USCCANA/netdiffuser-user2016 (slides)
• useR! 2016: https://github.com/USCCANA/netdiffuser-user2016

Examples

This example has been taken from the package’s vignettes:

r library(netdiffuseR)

Attaching package: 'netdiffuseR'

The following object is masked from 'package:base':

    %*%

Infectiousness and Susceptibility

``` r

Generating a random graph

set.seed(1234) n <- 100 nper <- 20 graph <- rgraph_er(n, nper, .5) toa <- sample(c(1:(1+nper-1), NA), n, TRUE) head(toa) ```

[1] 16  3 14  3 13  5

``` r

Creating a diffnet object

diffnet <- as_diffnet(graph, toa) diffnet ```

Dynamic network of class -diffnet-
 Name               : Diffusion Network
 Behavior           : Unknown
 # of nodes         : 100 (1, 2, 3, 4, 5, 6, 7, 8, ...)
 # of time periods  : 20 (1 - 20)
 Type               : directed
 Num of behaviors   : 1
 Final prevalence   : 0.95
 Static attributes  : -
 Dynamic attributes : -

r summary(diffnet)

Diffusion network summary statistics
 Name     :  Diffusion Network 
 Behavior : Unknown
-----------------------------------------------------------------------------
 Period   Adopters   Cum Adopt. (%)   Hazard Rate   Density   Moran's I (sd)  
-------- ---------- ---------------- ------------- --------- ---------------- 
       1          8         8 (0.08)             -      0.50 -0.01 (0.00)     
       2          3        11 (0.11)          0.03      0.50 -0.01 (0.00)     
       3          6        17 (0.17)          0.07      0.51 -0.01 (0.00)     
       4          3        20 (0.20)          0.04      0.49 -0.01 (0.00)     
       5          9        29 (0.29)          0.11      0.50 -0.01 (0.00)     
       6          5        34 (0.34)          0.07      0.50 -0.01 (0.00)     
       7          2        36 (0.36)          0.03      0.51 -0.01 (0.00)     
       8          3        39 (0.39)          0.05      0.50 -0.01 (0.00)     
       9          5        44 (0.44)          0.08      0.50 -0.01 (0.00)     
      10          1        45 (0.45)          0.02      0.49 -0.01 (0.00)     
      11          3        48 (0.48)          0.05      0.50 -0.01 (0.00)     
      12          6        54 (0.54)          0.12      0.50 -0.01 (0.00)     
      13          8        62 (0.62)          0.17      0.50 -0.01 (0.00)     
      14          9        71 (0.71)          0.24      0.50 -0.01 (0.00)     
      15          5        76 (0.76)          0.17      0.50 -0.00 (0.00) **  
      16          7        83 (0.83)          0.29      0.50 -0.01 (0.00)     
      17          5        88 (0.88)          0.29      0.49 -0.00 (0.00) *** 
      18          4        92 (0.92)          0.33      0.50 -0.01 (0.00)     
      19          1        93 (0.93)          0.12      0.50 -0.01 (0.00)     
      20          2        95 (0.95)          0.29      0.50 -0.01 (0.00)     
----------------------------------------------------------------------------- 
 Left censoring  : 0.08 (8) 
 Right centoring : 0.05 (5) 
 # of nodes      : 100

 Moran's I was computed on contemporaneous autocorrelation using 1/geodesic
 values. Significane levels  *** <= .01, ** <= .05, * <= .1.

``` r

Visualizing distribution of suscep/infect

out <- plot_infectsuscep(diffnet, bins = 20,K=5, logscale = FALSE, h=.01) ```

r out <- plot_infectsuscep(diffnet, bins = 20,K=5, logscale = TRUE, exclude.zeros = TRUE, h=1)

Warning in plot_infectsuscep.list(graph$graph, graph$toa, t0, normalize, : When
applying logscale some observations are missing.

Threshold

``` r

Generating a random graph

set.seed(123) diffnet <- rdiffnet(500, 20, seed.nodes = "random", rgraph.args = list(m=3), threshold.dist = function(x) runif(1, .3, .7)) diffnet ```

Dynamic network of class -diffnet-
 Name               : A diffusion network
 Behavior           : Random contagion
 # of nodes         : 500 (1, 2, 3, 4, 5, 6, 7, 8, ...)
 # of time periods  : 20 (1 - 20)
 Type               : directed
 Num of behaviors   : 1
 Final prevalence   : 1.00
 Static attributes  : real_threshold (1)
 Dynamic attributes : -

``` r

Threshold with fixed vertex size

plot_threshold(diffnet) ```

Using more features

r data("medInnovationsDiffNet") set.seed(131) plot_threshold( medInnovationsDiffNet, vertex.color = viridisLite::inferno(4)[medInnovationsDiffNet[["city"]]], vertex.sides = medInnovationsDiffNet[["city"]] + 2, sub = "Note: Vertices' sizes and shapes given by degree and city respectively", jitter.factor = c(1,1), jitter.amount = c(.25,.025) )

Warning in (function (graph, expo, toa, include_censored = FALSE, t0 = min(toa,
: -vertex.sides- will be coerced to integer.

Adoption rate

r plot_adopters(diffnet)

Hazard rate

r hazard_rate(diffnet)

Diffusion process

r plot_diffnet(medInnovationsDiffNet, slices=c(1,9,8))

r diffnet.toa(brfarmersDiffNet)[brfarmersDiffNet$toa >= 1965] <- NA plot_diffnet2(brfarmersDiffNet, vertex.size = "indegree")

``` r set.seed(1231)

Random scale-free diffusion network

x <- rdiffnet(1000, 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=1), layout=igraph::layoutwithfr)

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 <- viridisLite::plasma(100) par(mfrow=c(1,2), oma=c(1,0,0,0), cex=.8) 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) ```

r par(oldpar)

Adopters classification

r out <- classify(kfamilyDiffNet, include_censored = TRUE) ftable(out)

               thr Non-Adopters Very Low Thresh. Low Thresh. High Thresh. Very High Thresh.
toa                                                                                        
Non-Adopters               0.00             0.00        0.00         0.00              0.00
Early Adopters             0.00            14.04        8.40         0.57              0.29
Early Majority             0.00             5.64       11.65         5.54              2.58
Late Majority              0.00             1.34        5.06         6.21              2.96
Laggards                   0.00             1.53        0.00         0.00             34.19

``` r

Plotting

oldpar <- par(no.readonly = TRUE) par(xpd=TRUE) plot(out, color=viridisLite::inferno(5), las = 2, xlab="Time of Adoption", ylab="Threshold", main="")

Adding key

legend("bottom", legend = levels(out$thr), fill=viridisLite::inferno(5), horiz = TRUE, cex=.6, bty="n", inset=c(0,-.1)) ```

r par(oldpar)

Session info

r sessionInfo()

R version 4.5.0 (2025-04-11)
Platform: aarch64-apple-darwin24.2.0
Running under: macOS Sequoia 15.0.1

Matrix products: default
BLAS:   /opt/homebrew/Cellar/openblas/0.3.29/lib/libopenblasp-r0.3.29.dylib 
LAPACK: /opt/homebrew/Cellar/r/4.5.0/lib/R/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Denver
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] netdiffuseR_1.23.0

loaded via a namespace (and not attached):
 [1] Matrix_1.7-3          jsonlite_2.0.0        dplyr_1.1.4          
 [4] compiler_4.5.0        tidyselect_1.2.1      Rcpp_1.0.14          
 [7] networkLite_1.1.0     boot_1.3-31           yaml_2.3.10          
[10] fastmap_1.2.0         lattice_0.22-6        coda_0.19-4.1        
[13] R6_2.6.1              generics_0.1.4        MatchIt_4.7.2        
[16] igraph_2.1.4.9046     knitr_1.50            MASS_7.3-65          
[19] backports_1.5.0       tibble_3.3.0          statnet.common_4.12.0
[22] pillar_1.10.2         rlang_1.1.6           xfun_0.52            
[25] viridisLite_0.4.2     cli_3.6.5             magrittr_2.0.3       
[28] network_1.19.0        digest_0.6.37         grid_4.5.0           
[31] lifecycle_1.0.4       vctrs_0.6.5           sna_2.8              
[34] evaluate_1.0.3        SparseM_1.84-2        glue_1.8.0           
[37] rmarkdown_2.29        tools_4.5.0           pkgconfig_2.0.3      
[40] networkDynamic_0.11.5 htmltools_0.5.8.1    

To-do list

• Import/Export functions for interfacing other package’s clases, in particular: statnet set (specially the packages networkDynamic and ndtv), ~~igraph~~ and Rsiena.
• Populate the tests folder.
• ~~Use spells? (select_egoalter would use this)~~
• ~~Classify individuals by adoption category using early adopters, adopters, and laggards, and by threshold using very low, low, high and very high threshold (Valente 95’ p. 94).~~
• ~~Double check all functions using adjacency matrix values.~~
• ~~Remove dimnames from matrices and vectors. It is more efficient to use the ones stored in meta instead.~~
• Implement the Bass model
• ~~Include function to import survey data (as shown on the vignettes)~~
• Exposure based on Mahalanobis distances and also Roger Leenders on weighting exposure (internal note).
• (2016-03-30): use xspline for drawing polygons & edges.
• ~~(2016-04-04): Add more options to exposure, namely, self (so removes diagonal or not!).~~
• (2016-04-19): animal behaviorists.
• (2016-10-18): Review language throughout the manual (more than innovation).
• (2016-10-18): Evaluate and eventually use a standard graph format (network for instance?).
• (2016-10-18): Standarize graph plot methods (choose either statnet/igraph/own)