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moment superseded by the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 💩.
You can install the developers version of levelnet with:
# install.packages("remotes")
remotes::install_github("schochastics/levelnet")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)
#> name party bill
#> 1 Enzi, Michael B. R 115s1
#> 2 Cardin, Benjamin L. D 115s10
#> 3 Wicker, Roger F. R 115s10
#> 4 Alexander, Lamar R 115s100
#> 5 Franken, Al D 115s1000
#> 6 Murray, Patty D 115s1000
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)
#> name rmse_row rmse_col time
#> 1 logit 31.9029 3.52374 2.160
#> 2 probit 28.5854 3.27539 1.768
#> 3 cloglog 39.0993 4.20091 4.977
#> 4 scobit 15.1629 2.71215 16.215The 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)