In schochastics/levelnet: Methods to Analyze One-mode Projections of Two-mode Networks

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levelnet

~~levelnet is, so far, an early-stage R package that can be used to analyse two-mode networks and specifically their one-mode projection. The main purpose is to make several methods available that extract the binary backbone of a one-mode projection.~~ The package is, at the moment superseded by the backbone package. The only feature not implemented in {{backbone}} is the use of the scobit model in the stochastic degree sequence model (SDSM) by Z. Neal (link). More features will be added once the research is done r emo::ji("poo").

Installation

You can install the developers version of levelnet with:

# install.packages("remotes")
remotes::install_github("schochastics/levelnet")

Example of sdsm

The package includes bill-cosponsorship data of the 115th Senate. Similar data was used in the Paper by Neal an in follow up work. The function bipartite_from_data_frame can be used to turn the data frame into a two-mode network (bipartite graph).

library(igraph)
library(levelnet)
data("cosponsor_senate_15")
head(cosponsor_senate_15)

g <- bipartite_from_data_frame(cosponsor_senate_15,"name","bill")

The function sdsm_diagnostic checks the performance of several link functions.

params <- list(b0 = 1e-5,b1 = 1e-5,b2 = 1e-5,b3 = 1e-5,a = 0.8)
sdsm_diagnostic(g,verbose = FALSE,params = params)

The gradient function of the scobit model is very complicated and needs to be evaluated many times during the optimization process. This and the scobit function evaluation are the main bottleneck.

As was noted in the paper, the scobit model produces the best fit of the data.

params <- list(b0 = 1e-5,b1 = 1e-5,b2 = 1e-5,b3 = 1e-5,a = 0.8)
l <- sdsm(g,proj = "true",model = "scobit",params = params)

schochastics/levelnet documentation built on Feb. 3, 2023, 4:20 a.m.