TY - JOUR
T1 - Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization
AU - Luo, Xin
AU - Liu, Zhigang
AU - Shang, Mingsheng
AU - Lou, Jungang
AU - Zhou, Meng Chu
N1 - Funding Information:
Manuscript received October 7, 2020; revised November 10, 2020; accepted November 22, 2020. Date of publication November 25, 2020; date of current version March 17, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61772493, in part by the Natural Science Foundation of Chongqing (China) under Grants cstc2019jcyjjqX0013, and cstc2019jcyj-msxmX0578, in part by the Natural Science Foundation of Zhejiang Province under Grant LR20F020002, and in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences. Recommended for acceptance by Dr. Naixue Xiong. (Corresponding authors: Jungang Lou and MengChu Zhou.) Xin Luo and Mingsheng Shang are with the Chongqing Key Lab. of Big Data and Intelligent Computing, and the Chongqing Engineering Research Center of Big Data Application for Smart Cities, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences and also with the Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China (e-mail: luoxin21@cigit. ac.cn; msshang@cigit.ac.cn).
Publisher Copyright:
© 2013 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Community detection, aiming at determining correct affiliation of each node in a network, is a critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) is frequently adopted to handle this task. However, existing SNMF models mostly focus on a network's first-order topological information described by its adjacency matrix without considering the implicit associations among involved nodes. To address this issue, this study proposes a Pointwise mutual information-incorporated and Graph-regularized SNMF (PGS) model. It uses a) Pointwise Mutual Information to quantify implicit associations among nodes, thereby completing the missing but crucial information among critical nodes in a uniform way; b) graph-regularization to achieve precise representation of local topology, and c) SNMF to implement efficient community detection. Empirical studies on eight real-world social networks generated by industrial applications demonstrate that a PGS model achieves significantly higher accuracy gain in community detection than state-of-the-art community detectors.
AB - Community detection, aiming at determining correct affiliation of each node in a network, is a critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) is frequently adopted to handle this task. However, existing SNMF models mostly focus on a network's first-order topological information described by its adjacency matrix without considering the implicit associations among involved nodes. To address this issue, this study proposes a Pointwise mutual information-incorporated and Graph-regularized SNMF (PGS) model. It uses a) Pointwise Mutual Information to quantify implicit associations among nodes, thereby completing the missing but crucial information among critical nodes in a uniform way; b) graph-regularization to achieve precise representation of local topology, and c) SNMF to implement efficient community detection. Empirical studies on eight real-world social networks generated by industrial applications demonstrate that a PGS model achieves significantly higher accuracy gain in community detection than state-of-the-art community detectors.
KW - Community Detection
KW - Computational Intelligence
KW - Graph-regularization
KW - Network Representation
KW - Pointwise Mutual Information
KW - Social Network
KW - Symmetric and Non-negative Matrix Factorization
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U2 - 10.1109/TNSE.2020.3040407
DO - 10.1109/TNSE.2020.3040407
M3 - Article
AN - SCOPUS:85097183758
SN - 2327-4697
VL - 8
SP - 463
EP - 476
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
IS - 1
M1 - 9271912
ER -