rMultiNet introduces an extension R package that includes a variety of traditional and state-of-the-art tensor decomposition methods for mixture multilayer networks analysis.The package is developed with the modular pipeline mode: generative modeling, embedding algorithms and visualization. rMultiNet aims to help study complex networks, especially mixture multilayer networks.
Before using the rMultiNet, you need to install the following packages.
require(rTensor)
require(ggplot)
require(Matrix)
require(plotly)
Install the stable version of R-package from Git rMultiNet package directly with:
# devtools::install_github("ChenyuzZZ73/rMultiNet")
install.packages("rMultiNet", repos = c('https://chenyuzzz73.r-universe.dev', 'https://cloud.r-project.org'))
bulid()
library(rMultiNet)
The package is developed with the modular pipeline mode: generative modeling, embedding algorithms and visualization.
library(rMultiNet)
GenerateMMSBM(n, m, L, K, d = NULL, r = NULL)
GenerateMMLSM(n, m, L, rank, U mean= 0.5, cmax =1, d, int type = ‘Uniform’, kernel fun = ‘logit’, scale par=1)
Also, rMultiNet provides three datasets for study：human malaria parasite gene network, worldwide food trading network and UN Commodity trading network.
load("~/Desktop/rMultiNet/data/malariagene/malaria.RData")
Take one of the algorithms for example.
InitializationMMSBM(tnsr, ranks=NULL)
PowerIteration(tnsr, ranks=NULL, type=”TWIST”, U 0 list, delta1=1000, delta2=1000, max iter = 25, tol = 1e-05)
Embedding_network(network membership,L, paxis=2)
Community_cluster_km(embedding,type,cluster number)
Community_cluster_dbscan(embedding,type,eps value =.05, pts_value=5)
If you use rMultiNet or reference our tutorials in a presentation or publication, we would appreciate citations of our library.