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Hybrid adaptive modularized tri-factor non-negative matrix factorization for community detection in complex networks

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SCIENTIA IRANICA 2023年第3期30卷 1068-1084页

作者： Ghadirian, M. Bigdeli, N. Imam Khomeini Int Univ Fac Tech & Engn Dept Control Engn Qazvin Iran

Community detection is a significant issue in extracting valuable information and understanding complex network structures. Non-negative Matrix Factorization (NMF) methods are the most remarkable topics in community detection. The modularized trifactor NMF (Mtrinmf) method was proposed as a new class of NMF methods that combines the modularized information with tri-factor NMF. It had high computational complexity due to its dependence on the choice of the initial value of its parameter and the number of communities (c). In other words, the Mtrinmf method should search among different candidates to find correct c. In this paper, a novel Hybrid adaptive Mtrinmf (Hamtrinmf) method is proposed to improve the performance of Mtrinmf and reduce the computational complexity efficiently. In the proposed method, computational complexity reduction is made possible by selecting the right c candidates and tuning parameter. For this purpose, a hybrid algorithm including Singular Value Decomposition (SVD) and Relative Eigenvalue Gap (REG) algorithms is suggested to estimate the set of c candidates. Next, the Tuning parameter Mtrinmf (Tpmtrinmf) model is proposed to improve the performance of community detection via employing a self-tuning beta parameter. Moreover, experimental results confirm the efficiency of the Hamtrinmf method with respect to other reference methods on artificial and real-world networks. (c) 2023 Sharif University of Technology. All rights reserved.

关键词： Community detection Tuning parameter Non-negative matrix factorization modularized regularization Singular value decomposition

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modularized tri-factor nonnegative matrix factorization for community detection enhancement

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 533卷 122050-000页

作者： Yan, Chao Chang, Zhenhai Cent Univ Finance & Econ Sch Math & Stat Beijing 100081 Peoples R China Tianshui Normal Univ Sch Math & Stat Tianshui 741000 Peoples R China

Community structure detection is a fundamental problem for understanding the relationship between the topology structures and the functions of complex networks. NMF-based models are a promising method for identifying communities from networks, but most of them require the number of communities in advance, which is inconvenient for real applications. Also, the basic NMF model could not reflect the characteristics of networks more comprehensively under the sole nonnegative constraint. In this paper, we develop a novel modularized tri-factor nonnegative matrix factorization model which combines the modularized information as the regularization term, leading to improved performance in community detection. Besides, we utilize general modularity density to determine the number of communities. Finally, the effectiveness of our approach is demonstrated on both synthetic and real-world networks. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Community detection modularized regularization Nonnegative matrix factorization General modularity density

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