Nonnegative Matrix Factorization Based on Node Centrality for Community Detection

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 NMFbased 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 article 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 stateof-the-art methods.

Automated summary

This article proposes a community detection method based on node centrality under the framework of NMF. It designs a new similarity measure considering higher-order neighbors to form a more informative graph regularization mechanism and introduces node centrality and Gini impurity to measure the importance and sparseness of community memberships. Extensive experimental results demonstrate that the proposed method outperforms thirteen state-of-the-art methods on various real-world networks.