NetworkInference: NetworkInference: Inferring latent diffusion networks
In desmarais-lab/NetworkInference: Inferring Latent Diffusion Networks

Description Details Data preparation Estimation Interpretation and Visualization Performance

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.

The package provides three groups of functions: 1) data preparation 2) estimation and 3) interpretation.

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.

Diffusion networks are estimated using the netinf function. It produces a diffusion network in form of an edgelist (of class data.frame).

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.

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/.

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