Symmetry-constrained Non-negative Matrix Factorization Approach for Highly-Accurate Community Detection

Document Type

Conference Proceeding

Publication Date

8-23-2021

Abstract

A community structure is a fundamental property of complex networks and its detection plays an important role in exploring and understanding such networks. Due to its great interpretability, a symmetric and non-negative matrix factorization (SNMF) model is frequently adopted to perform community detection tasks. However, it adopts a single latent factor (LF) matrix to construct the approximation of a given undirected matrix to ensure its absolute symmetry at the expense of shrinking its solution space. This paper proposes a symmetry-constrained NMF (SCNMF) method, with two-fold ideas: a) modeling the approximate symmetry of an undirected network by introducing an equality-constraint on LF matrices into an NMF framework; and b) using graph-regularization to extract the features regarding the intrinsic geometric structure of a network. Extensively empirical studies on six real-world social networks from industrial applications demonstrate that the proposed SCNMF-based detector achieves higher accuracy for community detection than state-of-the-art models.

Identifier

85117032117 (Scopus)

ISBN

[9781665418737]

Publication Title

IEEE International Conference on Automation Science and Engineering

External Full Text Location

https://doi.org/10.1109/CASE49439.2021.9551446

e-ISSN

21618089

ISSN

21618070

First Page

1521

Last Page

1526

Volume

2021-August

Grant

CAAIXSJLJJ-2020-004B

Fund Ref

Chongqing University of Posts and Telecommunications

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