MultiNeSS

The R package “multiness” implements model fitting and simulation for Gaussian and logistic inner product MultiNeSS models for multiplex networks. The package uses a convex fitting algorithm with fully adaptive parameter tuning, including options for edge cross-validation. For more details see MacDonald et al., (2022).

Installation

You can install “multiness” version 1.0.2 from CRAN using

install.packages("multiness")

You can install the development version of “multiness” from GitHub using

devtools::install_github("peterwmacd/multiness")

Example

“multiness” includes an example multiplex network of agricultural trade which is studied in MacDonald et al., (2022). It is easy to import and to fit a Gaussian MultiNeSS model with adaptive tuning.

library(multiness)

# import data
data(agri_trade)
dim(agri_trade)
#> [1] 145 145  13

# log transformation for edge weights
A <- log(1+agri_trade)

# model fit
fit <- multiness_fit(A,model="gaussian",self_loops=FALSE,
                     tuning="adaptive",tuning_opts=list(penalty_const=3),
                     optim_opts=list(max_rank=100,return_posns=TRUE))

# inspect fitted latent space dimensions
# common latent space
fit$d1
#> [1] 30
# individual latent spaces
fit$d2
#>  [1]  2  4  4  3  4  8  5  5 16 11  4 12  6

# plot first two common latent dimensions
plot(fit$V_hat[,1:2],main="Common latent dimensions",
     xlab="v1",ylab="v2",xlim=c(0,4.5))
# label a subset of the points
countries <- dimnames(A)[[1]]
do_label <- c(4,5,8,10,11,14,17,19,20,24,25,28,33,34,35,37,39,41,54,61,75)
text(fit$V_hat[do_label,1],fit$V_hat[do_label,2],
     labels=countries[do_label],pos=4,cex=.8)