Mingyang Ren

Table of contents

  1. Description
  2. Methodology
  3. Quick Start


Consistent Estimation of the Number of Communities via Regularized Network Embedding: 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 pushes similar community centers collapsed with each other. A rigorous theoretical analysis is conducted, establishing asymptotic consistency in terms of community detection and estimation of the number of communities.


Model setting

Consider an undirected network with \(n\) nodes and a symmetric adjacency matrix \(\boldsymbol{A} = (a_{ij})^{n}_{i,j=1}\) with \(a_{ij} \in \{0,1\}\), where \(a_{ij}=1\) if there is an edge between node \(i\) and node \(j\), and \(a_{ij}=0\) otherwise. To incorporate node heterogeneity, we consider the network embedding model, \[\begin{equation}\label{generate} \begin{aligned} a_{i j}=a_{j i} \stackrel{i n d .}{\sim} \operatorname{Bernoulli} (p_{i j}) \ \text{with} \ \operatorname{logit}\left(p_{i j}\right)=\log \frac{p_{i j}}{1-p_{i j}}=\boldsymbol{z}_{i}^{T} \boldsymbol{z}_{j} , \end{aligned} \end{equation}\] for any \(i < j\), where \(\boldsymbol{z}_{i}\) is the embedding vector of node \(i\) in an \(r\)-dimensional space with \(r \ll n\). The embedding vectors preserve the inherent structure of the undirected network, in that the embedding vectors of similar nodes shall be close as well. Consequently, a community in the undirected network correspond to a subset of nodes whose embedding vectors may vary one from another but are all tightly clustered together in the same neighborhood. Specifically, each node \(i\) with the embedding vector \(\boldsymbol{z}_{i}\) corresponds to a community center, denoted by \(\boldsymbol{b}_i\), and two nodes \(i\) and \(j\) are said to be in the same community if and only if they share the same community center, \(\boldsymbol{b}_i = \boldsymbol{b}_j\).

Reguarlized network embedding

We propose a regularized network embedding model to jointly estimate the community structure and the unknown community number. Let \(\boldsymbol{Z} = ( \boldsymbol{z}_1, \cdots, \boldsymbol{z}_n )^{\top}\), and \(L(\boldsymbol{Z}; \boldsymbol{A}) = \frac{1}{n^2} \sum_{i,j=1}^{n}\left\{ \log [ 1+\exp (\boldsymbol{z}_{i}^{\top} \boldsymbol{z}_{j} )] - \boldsymbol{z}_{i}^{\top} \boldsymbol{z}_{j} a_{i j} \right\}\) denotes the negative log-likelihood of the network embedding model in (\(\ref{generate}\)). The proposed model is formulated as a form of regularization likelihood, \[\begin{equation} \begin{aligned} \min_{\boldsymbol{Z}, \boldsymbol{B}} Q( \boldsymbol{Z}, \boldsymbol{B}; \boldsymbol{A} ) & = L(\boldsymbol{Z}; \boldsymbol{A}) + J_1 (\boldsymbol{Z}, \boldsymbol{B}) + J_2 ( \boldsymbol{B}) + J_3 (\boldsymbol{Z}), \\ J_1 (\boldsymbol{Z}, \boldsymbol{B}) & = \lambda_1 \| \boldsymbol{Z} - \boldsymbol{B} \|_F^2, \\ J_2 (\boldsymbol{B}) & = \sum_{i<j} \omega_{ij} p ( \| \boldsymbol{b}_i - \boldsymbol{b}_j \|_2; \lambda_2), \\ J_3 (\boldsymbol{Z}) & = \sum_{m=1}^{r^{\prime}} p ( \| \boldsymbol{Z}_{(m)} \|_2; \lambda_3 ), \end{aligned} \end{equation}\] where \(\boldsymbol{Z}_{(m)}\) is the \(m\)-th column of \(\boldsymbol{Z}\), \(\boldsymbol{B} = ( \boldsymbol{b}_1, \cdots, \boldsymbol{b}_n )^{\top}\) with community center \(\boldsymbol{b}_i \in \mathbb{R}^{r}\), \(\omega_{ij} \in \{ 0, 1\}\) determines the weight on the fusion penalty between \(\boldsymbol{b}_i\) and \(\boldsymbol{b}_j\), \(\lambda_1\) and \(\lambda_2\) are two positive tuning parameters, and \(p(t ; \lambda)\) can be any concave regularization term. We adopt the minimax concave penalty (MCP). As for \(\omega_{ij}\), a natural choice is to set \(\omega_{ij}=1\) for all \((i,j)\) pairs.

Quick Start

First, we call the built-in simulation data set (\(K^* = 4\)) and the sequences of the tuning parameters (\(\lambda_1\), \(\lambda_2\), and \(\lambda_3\)).

# 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,

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)