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This package provides an R implementation of the netinf algorithm created by @gomez2010inferring (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:
The latest development version can be installed from github:
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:
| origin_node | destination_node | improvement | p_value |
|---|---|---|---|
| 12 | 3 | 310.1 | 2.693e-06 |
| 8 | 12 | 264.4 | 1.99e-05 |
| 15 | 13 | 246.4 | 4.715e-05 |
| 3 | 6 | 231.4 | 0.0001109 |
| 19 | 8 | 230.3 | 0.0001127 |
| 17 | 11 | 224.9 | 0.0001122 |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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