cencrne

An R package for consistent estimation of the number of communities via regularized network embedding.

Introduction

The network analysis plays an important role in numerous application domains including biomedicine. Estimation of the number of communities is a fundamental and critical issue in network analysis. Most existing studies assume that the number of communities is known a priori, or lack of rigorous theoretical guarantee on the estimation consistency. This method proposes a regularized network embedding model to simultaneously estimate the community structure and the number of communities in a unified formulation. The proposed model equips network embedding with a novel composite regularization term, which pushes the embedding vector towards its center and collapses similar community centers with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities.

Publication

Ren, M., Zhang S. and Wang J. (2022+). Consistent Estimation of the Number of Communities via Regularized Network Embedding. Manuscript.

Maintainer

Mingyang Ren renmingyang17@mails.ucas.ac.cn

Installation

Method 1: Run the following codes directly in R.

library("devtools")
devtools::install_github("Ren-Mingyang/cencrne")

Method 2: Download the cencrne_1.0.0.tar.gz, and install from Package Archive File using RStudio.

Usage

To make the package more user-friendly, there are detailed help documents and vignettes in the package, which can be referred to after the installation.

Quick Start

First, we call the built-in simulation data set (K* = 4) and the sequences of the tuning parameters (lambda1, lambda2, and lambda3).

library(cencrne)
# example.data
data(example.data)
A                   = example.data$A
K.true              = example.data$K.true
Z.true              = example.data$Z.true
B.true              = example.data$B.true
P.true              = example.data$P.true
Theta.true          = example.data$Theta.true
cluster.matrix.true = example.data$cluster.matrix.true

n       = dim(A)[1]
lam.max = 3
lam.min = 0.5
lam1.s  = 2/log(n)
lam2.s  = sqrt(8*log(n)/n)
lam3.s  = 1/8/log(n)/sqrt(n)
lambda  = genelambda.obo(nlambda1=3,lambda1_max=lam.max*lam1.s,lambda1_min=lam.min*lam1.s,
                         nlambda2=10,lambda2_max=lam.max*lam2.s,lambda2_min=lam.min*lam2.s,
                         nlambda3=1,lambda3_max=lam.max*lam3.s,lambda3_min=lam.min*lam3.s)

Apply the proposed method.

sample.index.n = rbind(combn(n,2),1:(n*(n-1)/2))
int.list       = gen.int(A)
Z.int          = int.list$Z.int
B.int          = int.list$B.int
res            = network.comm.num(A, sample.index.n, lambda, Z.int, B.int)

# output results
K.hat = res$Opt_K # the estimated number of communities
Z.hat = res$Opt_Z # the estimated embedding vectors corresponding to n nodes
cluster.matrix.hat = res$Opt_cluster.matrix # the n * n estimated membership matrix
evaluation(Z.hat, Z.true, cluster.matrix.hat, cluster.matrix.true,
           P.true, Theta.true, K.hat, K.true)

The algorithm is efficient, and it takes less than ten minutes for the toy example.