A Survey of Community Detection in Complex Networks Using Nonnegative Matrix Factorization

Chaobo He, Xiang Fei, Qiwei Cheng, Hanchao Li, Zeng Hu, Yong Tang

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    41 Citations (Scopus)
    725 Downloads (Pure)


    Community detection is one of the popular research topics in the field of complex networks analysis. It aims to identify communities, represented as cohesive subgroups or clusters, where nodes in the same community link to each other more densely than others outside. Due to the interpretability, simplicity, flexibility, and generality, nonnegative matrix factorization (NMF) has become a very ideal model for community detection and lots of related methods have been presented. To facilitate research on NMF-based community detection, in this article, we make a comprehensive review on NMF-based methods for community detection, especially the state-of-the-art methods presented in high prestige journals or conferences. First, we introduce the basic principles of NMF and explain why NMF can detect communities and design a general framework of NMF-based community detection. Second, according to the applicable network types, we propose a taxonomy to divide the existing NMF-based methods for community detection into six categories, namely, topology networks, signed networks, attributed networks, multilayer networks, dynamic networks, and large-scale networks. We deeply analyze representative methods in every category. Finally, we summarize the common problems faced by all methods and potential solutions and propose four promising research directions. We believe that this survey can fully demonstrate the versatility of NMF-based community detection and serve as a useful guideline for researchers in related fields.

    Original languageEnglish
    Pages (from-to)440-457
    Number of pages18
    JournalIEEE Transactions on Computational Social Systems
    Issue number2
    Early online date5 Oct 2021
    Publication statusPublished - 1 Apr 2022

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    10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62077045, U1811263 and 61772211)
    Humanity and Social Science Youth Foundation of Ministry of Education of China (Grant Number: 19YJCZH049)
    10.13039/501100001809-Natural Science Foundation of Guangdong Province of China (Grant Number: 2019A1515011292)


    • Attributed networks
    • community detection
    • Complex networks
    • complex networks
    • dynamic networks
    • Image edge detection
    • large-scale networks
    • Mathematical models
    • Matrix decomposition
    • multilayer networks
    • nonnegative matrix factorization (NMF)
    • signed networks
    • Social networking (online)
    • Taxonomy
    • topology networks.
    • Transforms

    ASJC Scopus subject areas

    • Modelling and Simulation
    • Social Sciences (miscellaneous)
    • Human-Computer Interaction


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