Nonnegative Matrix Factorization Based on Node Centrality for Community Detection

Abstract

Community detection is an important topic in network analysis, and recently many community detection methods have been developed on top of the Nonnegative Matrix Factorization (NMF) technique. Most NMF-based community detection methods only utilize the first-order proximity information in the adjacency matrix, which has some limitations. Besides, many NMF-based community detection methods involve sparse regularizations to promote clearer community memberships. However, in most of these regularizations, different nodes are treated equally, which seems unreasonable. To dismiss the above limitations, this paper proposes a community detection method based on node centrality under the framework of NMF. Specifically, we design a new similarity measure which considers the proximity of higher-order neighbors to form a more informative graph regularization mechanism, so as to better refine the detected communities. Besides, we introduce the node centrality and Gini impurity to measure the importance of nodes and sparseness of the community memberships, respectively. Then, we propose a novel sparse regularization mechanism which forces nodes with higher node centrality to have smaller Gini impurity. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over thirteen state-of-the-art methods.

Symmetric NMF with Information-Theoretic Metric Learning regularization for Graph Clustering

“…Consequently, various extensions have been proposed for problem (2) in terms of loss function and regularization used [3]. In terms of applications, network analysis [4], community detection [5], [6], [3], and data clustering [7] applications are the main fields of application for symmetric NMF.…”

An Algorithm Based on Non-Negative Matrix Factorization for Detecting Communities in Networks

“…Presently, community detection methods based on NMF are evaluated based on two primary perspectives. One aspect to consider is the parameterization of the method, which often includes setting values for various parameters used in NMF-based algorithms [9][10][11]. These parameters often have reasonable default values.…”