TY - GEN
T1 - Symmetry-constrained Non-negative Matrix Factorization Approach for Highly-Accurate Community Detection
AU - Liu, Zhigang
AU - Luo, Xin
AU - Zhou, Meng Chu
N1 - Funding Information:
*This research is supported in part by CAAI-Huawei MindSpore Open Fund under grant CAAIXSJLJJ-2020-004B, in part by Chongqing Research Program of Technology Innovation and Application under grant cstc2019jscx-fxydX0027, and in part by Doctoral Student Talent Training Program of Chongqing University of Posts and Telecommunications under grant BYJS202009. (Corresponding Authors: X. Luo) Z. Liu is with the School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China, and also with the Chongqing School, University of Chinese Academy of Sciences, Chongqing 400714, China (email: liuzhigangx@gmail.com).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/23
Y1 - 2021/8/23
N2 - 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.
AB - 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.
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U2 - 10.1109/CASE49439.2021.9551446
DO - 10.1109/CASE49439.2021.9551446
M3 - Conference contribution
AN - SCOPUS:85117032117
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1521
EP - 1526
BT - 2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021
PB - IEEE Computer Society
T2 - 17th IEEE International Conference on Automation Science and Engineering, CASE 2021
Y2 - 23 August 2021 through 27 August 2021
ER -