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https://github.com/usccana/netdiffuser
netdiffuseR: Analysis of Diffusion and Contagion Processes on Networks
https://github.com/usccana/netdiffuser
contagion diffusion-network network-analysis network-visualization r
Last synced: about 15 hours ago
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netdiffuseR: Analysis of Diffusion and Contagion Processes on Networks
- Host: GitHub
- URL: https://github.com/usccana/netdiffuser
- Owner: USCCANA
- License: other
- Created: 2014-12-19T00:44:59.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2024-12-06T05:49:11.000Z (22 days ago)
- Last Synced: 2024-12-27T20:10:03.516Z (about 15 hours ago)
- Topics: contagion, diffusion-network, network-analysis, network-visualization, r
- Language: R
- Homepage: https://USCCANA.github.io/netdiffuseR
- Size: 99.8 MB
- Stars: 86
- Watchers: 16
- Forks: 22
- Open Issues: 27
-
Metadata Files:
- Readme: README.Rmd
- Changelog: ChangeLog
- License: LICENSE
Awesome Lists containing this project
README
---
output:
github_document:
html_preview: false
---
[](https://github.com/USCCANA/netdiffuseR/actions/workflows/r.yml)
[](https://app.codecov.io/github/USCCANA/netdiffuseR?branch=master)
[](https://cran.r-project.org/package=netdiffuseR)
[](https://cran.r-project.org/package=netdiffuseR)
[](https://cran.r-project.org/package=netdiffuseR)
[](https://doi.org/10.5281/zenodo.1039317)
[](https://cran.r-project.org/package=netdiffuseR)
[](https://pphs.usc.edu/center/cana/)
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](https://www.sciencedirect.com/science/article/pii/S027795361530143X))
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, comment = ""}
citation(package="netdiffuseR")
```
## News
Changelog can be view [here](NEWS.md).
* [2016-06-02] A video of the __netdiffuseR__ workshop at SUNBELT 2016 is now online on [youtube](https://www.youtube.com/playlist?list=PLT-GgRN1lFI4coHDqkRJm3flDw9e1gg2P), and the workshop materials can be found [here](https://github.com/USCCANA/netdiffuser-sunbelt2016/)
* [2016-04-11] __netdiffuseR__ will be on [useR! 2016](https://user2016.r-project.org/) on as a presentation and on [IC2S2 2016](https://www.kellogg.northwestern.edu/news-events/conference/ic2s2/2016.aspx) in the posters session.
* [2016-03-16] Next CRAN release scheduled for April 11th 2016 (after the workshop).
* [2016-02-18] __netdiffuseR__ vers 1.16.2 is now on CRAN!
## 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 `devtools` package, you can install `netdiffuseR` dev version as follows
```r
devtools::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](https://github.com/USCCANA/netdiffuseR/issues/3),
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](https://github.com/USCCANA/netdiffuseR/releases), netdiffuseR_1...zip, and netdiffuseR_1...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. Here are the repositories:
* Sunbelt 2018: https://usccana.github.io/netdiffuser-sunbelt2018/ ([source code](https://github.com/USCCANA/netdiffuser-sunbelt2018))
* NASN 2017: https://usccana.github.io/netdiffuser-nasn2017/ ([source code](https://github.com/USCCANA/netdiffuser-nasn2017))
* Sunbelt 2016: https://github.com/USCCANA/netdiffuser-sunbelt2016
## 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 loading}
library(netdiffuseR)
```
### Infectiousness and Susceptibility
```{r plot_infectsuscept}
# 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)
# Creating a diffnet object
diffnet <- as_diffnet(graph, toa)
diffnet
summary(diffnet)
# Visualizing distribution of suscep/infect
out <- plot_infectsuscep(diffnet, bins = 20,K=5, logscale = FALSE, h=.01)
out <- plot_infectsuscep(diffnet, bins = 20,K=5, logscale = TRUE,
exclude.zeros = TRUE, h=1)
```
### Threshold
```{r BoringThreshold, plot_threshold, fig.height=7}
# 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
# Threshold with fixed vertex size
plot_threshold(diffnet)
```
Using more features
```{r NiceThreshold, cache=FALSE}
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)
)
```
### Adoption rate
```{r Adopters}
plot_adopters(diffnet)
```
### Hazard rate
```{r Hazard}
hazard_rate(diffnet)
```
### Diffusion process
```{r plot_diffnet, fig.width=7, fig.height=4, message=FALSE}
plot_diffnet(medInnovationsDiffNet, slices=c(1,9,8))
```
```{r plot_diffnet2}
diffnet.toa(brfarmersDiffNet)[brfarmersDiffNet$toa >= 1965] <- NA
plot_diffnet2(brfarmersDiffNet, vertex.size = "indegree")
```
```{r plot_diffnet2 with map, }
set.seed(1231)
# Random scale-free diffusion network
x <- rdiffnet(1000, 4, seed.graph="scale-free", seed.p.adopt = .025,
rewire = FALSE, seed.nodes = "central",
rgraph.arg=list(self=FALSE, m=4),
threshold.dist = function(id) runif(1,.2,.4))
# Diffusion map (no random toa)
dm0 <- diffusionMap(x, kde2d.args=list(n=150, h=1), layout=igraph::layout_with_fr)
# Random
diffnet.toa(x) <- sample(x$toa, size = nnodes(x))
# Diffusion map (random toa)
dm1 <- diffusionMap(x, layout = dm0$coords, kde2d.args=list(n=150, h=.5))
oldpar <- par(no.readonly = TRUE)
col <- viridisLite::plasma(100)
par(mfrow=c(1,2), oma=c(1,0,0,0), cex=.8)
image(dm0, col=col, main="Non-random Times of Adoption\nAdoption from the core.")
image(dm1, col=col, main="Random Times of Adoption")
par(mfrow=c(1,1))
mtext("Both networks have the same distribution on times of adoption", 1,
outer = TRUE)
par(oldpar)
```
### Adopters classification
```{r mosaic}
out <- classify(kfamilyDiffNet, include_censored = TRUE)
ftable(out)
# Plotting
oldpar <- par(no.readonly = TRUE)
par(xpd=TRUE)
plot(out, color=viridisLite::inferno(5), las = 2, xlab="Time of Adoption",
ylab="Threshold", main="")
# Adding key
legend("bottom", legend = levels(out$thr), fill=viridisLite::inferno(5), horiz = TRUE,
cex=.6, bty="n", inset=c(0,-.1))
par(oldpar)
```
### Session info
```{r Showing session info}
sessionInfo()
```
## 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)