| NetworkInference {NetworkInference} | R Documentation |
NetworkInference: Inferring latent diffusion networks
Description
This package provides an R implementation of the netinf algorithm
created by Gomez Rodriguez, Leskovec, and Krause (2010). 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.
Details
The package provides three groups of functions: 1) data preparation 2) estimation and 3) interpretation.
Data preparation
The core estimation function netinf requires an object of class
cascade (see as_cascade_long and as_cascade_wide).
Cascade data contains information on the potential nodes in the network as
well as on event times for each node in each cascade.
Estimation
Diffusion networks are estimated using the netinf function. It
produces a diffusion network in form of an edgelist (of class
data.frame).
Interpretation and Visualization
Cascade data can be visualized with the plot method of the cascade
class (diffnet, plot.cascade). Results of the estimation process can
be visualized using the plotting method of the diffnet class.
Performance
If higher performance is required and for very large data sets, a faster pure C++ implementation is available in the Stanford Network Analysis Project (SNAP). The software can be downloaded at http://snap.stanford.edu/netinf/.