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)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
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