README.md

In desmarais-lab/NetworkInference: Inferring Latent Diffusion Networks

NetworkInference: Inferring Latent Diffusion Networks

About

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.

Installation

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')

Quick start guide

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 |

desmarais-lab/NetworkInference documentation built on May 15, 2019, 5:05 a.m.