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Semi-supervised community detection on attributed networks using non-negative matrix tri-factorization with node popularity

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Abstract

The World Wide Web generates more and more data with links and node contents, which are always modeled as attributed networks. The identification of network communities plays an important role for people to understand and utilize the semantic functions of the data. A few methods based on non-negative matrix factorization (NMF) have been proposed to detect community structure with semantic information in attributed networks. However, previous methods have not modeled some key factors (which affect the link generating process together), including prior information, the heterogeneity of node degree, as well as the interactions among communities. The three factors have been demonstrated to primarily affect the results. In this paper, we propose a semi-supervised community detection method on attributed networks by simultaneously considering these three factors. First, a semi-supervised non-negative matrix tri-factorization model with node popularity (i.e., PSSNMTF) is designed to detect communities on the topology of the network. And then node contents are integrated into the PSSNMTF model to find the semantic communities more accurately, namely PSSNMTFC. Parameters of the PSSNMTFC model is estimated by using the gradient descent method. Experiments on some real and artificial networks illustrate that our new method is superior over some related state-of-the-art methods in terms of accuracy.

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Grant Nos. 61876128, 61772361) and the National Science Foundation of Hebei (F2019403070) and the science and technology research project for universities of Hebei (ZD2020175).

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Di Jin, Jing He & Dongxiao He

  2. Department of Information Engineering, Hebei GEO University, Shijiazhuang, 050031, China

    Bianfang Chai

Authors
  1. Di Jin
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  2. Jing He
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  3. Bianfang Chai
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  4. Dongxiao He
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Corresponding author

Correspondence to Bianfang Chai.

Additional information

Di Jin received his BS, MS, and PhD degrees in computer science from Jilin University, China in 2005, 2008, and 2012, respectively. He was a postdoctoral research fellow at the School of Design, Engineering, and Computer, Bournemouth University, U.K., from 2013 to 2014. He is currently an associate professor with the College of Intelligence and Computing, Tianjin University, China. He has published more than 50 papers in international journals and conferences in the areas of community detection, social network analysis, and machine learning.

Jing He received the BS degrees from Guangxi University, China in 2016 and MS degree in computer science and technology from Tianjin University, China in 2020. Her research interests are mainly related to community detection on social networks.

Bianfang Chai received the BE and ME degrees in computer science from Hebei University, China in 2002 and 2006 respectively, and the PhD degree from Beijing Jiaotong University, China in 2015. She is an associate professor with the School of Information Engineering, Hebei GEO University, China. Her current research interests include community detection, semi-supervised clustering, probabilistic graphical model, and complex network analysis.

Dongxiao He received her BS, MS, and PhD degrees in computer science from Jilin University, China in 2007, 2010, and 2014, respectively. She was a post-doctoral research fellow in Department of Computer Science, Dresden University of Technology, Germany from 2014 to 2015. She is an associate professor with the College of Intelligence and Computing, Tianjin University, China. She has published over 40 international journal and conference papers. Her current research interests include data mining and analysis of complex networks.

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Semi-supervised community detection on attributed networks using non-negative matrix tri-factorization with node popularity

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Jin, D., He, J., Chai, B. et al. Semi-supervised community detection on attributed networks using non-negative matrix tri-factorization with node popularity. Front. Comput. Sci. 15, 154324 (2021). https://doi.org/10.1007/s11704-020-9203-0

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