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<h1>

netdiffuseR: Analysis of Diffusion and Contagion Processes on
Networks<img src="man/figures/netdiffuser-logo.svg" style="max-width:200px;width:50%;" align="right">
</h1>

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](https://doi.org/10.1016/j.socscimed.2015.10.001)).

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 1.25.0, <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](NEWS.md).

## 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). You can also get the package (pre-built binaries)
from our R-Universe repository (recommended):

``` r
install.packages(
  'epiworldR',
  repos = c(
    'https://uofuepibio.r-universe.dev',
    'https://cloud.r-project.org'
  )
)
```

## 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)
#> Thank you for using netdiffuseR! Please consider citing it in your work.
#> You can find the citation information by running
#>   citation("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

# 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 : -
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.

# Visualizing distribution of suscep/infect
out <- plot_infectsuscep(diffnet, bins = 20,K=5, logscale = FALSE, h=.01)
```

<img src="man/figures/README-plot_infectsuscept-1.png"
style="width:100.0%" />

``` 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.
```

<img src="man/figures/README-plot_infectsuscept-2.png"
style="width:100.0%" />

### 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 : -

# Threshold with fixed vertex size
plot_threshold(diffnet)
```

<img src="man/figures/README-BoringThreshold-1.png"
style="width:100.0%" />

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.
```

<img src="man/figures/README-NiceThreshold-1.png"
style="width:100.0%" />

### Adoption rate

``` r
plot_adopters(diffnet)
```

<img src="man/figures/README-Adopters-1.png" style="width:100.0%" />

### Hazard rate

``` r
hazard_rate(diffnet)
```

<img src="man/figures/README-Hazard-1.png" style="width:100.0%" />

### Diffusion process

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

<img src="man/figures/README-plot_diffnet-1.png" style="width:100.0%" />

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

<img src="man/figures/README-plot_diffnet2-1.png"
style="width:100.0%" />

``` 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::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 <- 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)
```

<img src="man/figures/README-plot_diffnet2-withmap-1.png"
style="width:100.0%" />

``` 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

# 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))
```

<img src="man/figures/README-mosaic-1.png" style="width:100.0%" />

``` r
par(oldpar)
```

### Session info

``` r
sessionInfo()
#> R version 4.5.3 (2026-03-11)
#> Platform: aarch64-unknown-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/aarch64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/aarch64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] netdiffuseR_1.25.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] Matrix_1.7-5          jsonlite_2.0.0        dplyr_1.2.0          
#>  [4] compiler_4.5.3        tidyselect_1.2.1      Rcpp_1.1.1           
#>  [7] networkLite_1.1.0     boot_1.3-32           yaml_2.3.12          
#> [10] fastmap_1.2.0         lattice_0.22-9        coda_0.19-4.1        
#> [13] R6_2.6.1              generics_0.1.4        MatchIt_4.7.2        
#> [16] igraph_2.2.2          knitr_1.51            MASS_7.3-65          
#> [19] backports_1.5.0       tibble_3.3.1          statnet.common_4.13.0
#> [22] pillar_1.11.1         rlang_1.1.7           xfun_0.56            
#> [25] otel_0.2.0            viridisLite_0.4.3     cli_3.6.5            
#> [28] magrittr_2.0.4        network_1.20.0        digest_0.6.39        
#> [31] grid_4.5.3            lifecycle_1.0.5       vctrs_0.7.2          
#> [34] SparseM_1.84-2        sna_2.8               evaluate_1.0.5       
#> [37] glue_1.8.0            rmarkdown_2.31        tools_4.5.3          
#> [40] pkgconfig_2.0.3       networkDynamic_0.11.5 htmltools_0.5.9
```

## 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)
