CSpace
A High-Order Proximity-Incorporated Nonnegative Matrix Factorization-Based Community Detector
Liu, Zhigang1,2; Yi, Yugen3; Luo, Xin4
2023-01-09
摘要Community describes the functional mechanism of an undirected network, making community detection a fundamental tool for graph representation learning-related applications like social circle discovery. To date, a Symmetric and Nonnegative Matrix Factorization (SNMF) model has been frequently adopted to address this issue owing to its high interpretability and scalability. However, most existing SNMF-based community detectors neglect the high-order proximity in an undirected network, thus suffering from accuracy loss caused by incomplete information. Motivated by this discovery, this paper proposes a High-Order Proximity-incorporated Nonnegative Matrix Factorization (HOP-NMF)-based community detector with the following three-fold ideas: a) adopting a weighted pointwise mutual information-based approach to measure the high-order proximity among nodes in a network; b) leveraging an iterative network enhancement scheme to encode the captured high-order proximity into the network to effectively enhance the its information; and c) implementing a capacity-enlarged and graph-regularized factorization algorithm for highly-accurate representation to the enhanced network. With the above design, an HOP-NMF model is able to achieve highly-accurate community detection results. Theoretical proof is rigorously conducted to validate its convergence ability. Extensively empirical studies on eight real networks from industrial applications demonstrate that an HOP-NMF-based community detector significantly outperforms sophisticated and state-of-the-art community detectors in detection accuracy.
关键词Detectors Symmetric matrices Indexes Training Probability Network analyzers Frequency measurement Learning system network representation learning community detection nonnegative matrix factorization symmetric model capacity graph regularization
DOI10.1109/TETCI.2022.3230930
发表期刊IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
ISSN2471-285X
页码15
通讯作者Luo, Xin(luoxin@swu.edu.cn)
收录类别SCI
WOS记录号WOS:000921097100001
语种英语