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Community Detection of Multi-Layer Attributed Networks via Penalized Alternating Factorization

Author

Listed:
  • Jun Liu

    (KLAS of MOE & School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China)

  • Jiangzhou Wang

    (KLAS of MOE & School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China)

  • Binghui Liu

    (KLAS of MOE & School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China)

Abstract

Communities are often associated with important structural characteristics of a complex network system, therefore detecting communities is considered to be a fundamental problem in network analysis. With the development of data collection technology and platform, more and more sources of network data are acquired, which makes the form of network as well as the related data more complex. To achieve integrative community detection of a multi-layer attributed network that involves multiple network layers together with their attribute data, effectively utilizing the information from the multiple networks and the attributes may greatly enhance the accuracy of community detection. To this end, in this article, we study the integrative community detection problem of a multi-layer attributed network from the perspective of matrix factorization, and propose a penalized alternative factorization (PAF) algorithm to resolve the corresponding optimization problem, followed by the convergence analysis of the PAF algorithm. Results of the numerical study, as well as an empirical analysis, demonstrate the advantages of the PAF algorithm in community discovery accuracy and compatibility with multiple types of network-related data.

Suggested Citation

  • Jun Liu & Jiangzhou Wang & Binghui Liu, 2020. "Community Detection of Multi-Layer Attributed Networks via Penalized Alternating Factorization," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:239-:d:320215
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    References listed on IDEAS

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    1. Michael T. SCHAUB & Jean-Charles DELVENNE, 2017. "The many facets of community detection in complex networks," LIDAM Reprints CORE 2890, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Ting Yan & Binyan Jiang & Stephen E. Fienberg & Chenlei Leng, 2019. "Statistical Inference in a Directed Network Model With Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 857-868, April.
    3. M. E. J. Newman & Aaron Clauset, 2016. "Structure and inference in annotated networks," Nature Communications, Nature, vol. 7(1), pages 1-11, September.
    4. James Moody & Douglas R. White, 2000. "Structural Cohesion and Embeddedness: A Hierarchical Conception of Social Groups," Working Papers 00-08-049, Santa Fe Institute.
    5. Catherine Matias & Vincent Miele, 2017. "Statistical clustering of temporal networks through a dynamic stochastic block model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1119-1141, September.
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