This package provides an R implementation of the netinf algorithm created by Gomez-Rodriguez, Leskovec, and Krause (see here for more information and the original C++ implementation). Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.
The package can be installed from CRAN:
install.packages("NetworkInference")The latest development version can be installed from github:
#install.packages(devtools)
devtools::install_github('desmarais-lab/NetworkInference')To get started, get your data into the cascades format required by the
netinf function:
library(NetworkInference)
# Simulate random cascade data
df <- simulate_rnd_cascades(50, n_node = 20)
# Cast data into `cascades` object
## From long format
cascades <- as_cascade_long(df)
## From wide format
df_matrix <- as.matrix(cascades) ### Create example matrix
cascades <- as_cascade_wide(df_matrix)Then fit the model:
result <- netinf(cascades, quiet = TRUE, p_value_cutoff = 0.05)head(result)| origin_node | destination_node | improvement | p_value |
|---|---|---|---|
| 20 | 7 | 290.1 | 7.324e-06 |
| 8 | 17 | 272 | 1.875e-05 |
| 3 | 2 | 270.5 | 1.87e-05 |
| 20 | 5 | 262.8 | 1.899e-05 |
| 7 | 16 | 250.4 | 4.779e-05 |
| 20 | 15 | 249 | 4.774e-05 |