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Community detection with attributed random walk via seed replacement

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Abstract

Community detection methods based on random walks are widely adopted in various network analysis tasks. It could capture structures and attributed information while alleviating the issues of noises. Though random walks on plain networks have been studied before, in real-world networks, nodes are often not pure vertices, but own different characteristics, described by the rich set of data associated with them. These node attributes contain plentiful information that often complements the network, and bring opportunities to the random-walk-based analysis. However, node attributes make the node interactions more complicated and are heterogeneous with respect to topological structures. Accordingly, attributed community detection based on random walk is challenging as it requires joint modelling of graph structures and node attributes. To bridge this gap, we propose a Community detection with Attributed random walk via Seed replacement (CAS). Our model is able to conquer the limitation of directly utilize the original network topology and ignore the attribute information. In particular, the algorithm consists of four stages to better identify communities. (1) Select initial seed nodes in the network; (2) Capture the better-quality seed replacement path set; (3) Generate the structure-attribute interaction transition matrix and perform the colored random walk; (4) Utilize the parallel conductance to expand the communities. Experiments on synthetic and real-world networks demonstrate the effectiveness of CAS.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61762078, 61363058, 61966004, 61966009,U1711263,U1811264), Natural Science Foundation of Gansu Province (21JR7RA114), Northwest Normal University Young Teachers Research Capacity Promotion Plan (NWNU-LKQN2019-2) and Research Fund of Guangxi Key Laboratory of Trusted Software (kx202003).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

    Yang Chang & Huifang Ma

  2. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Huifang Ma & Zhixin Li

  3. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Liang Chang

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  1. Yang Chang
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  2. Huifang Ma
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  3. Liang Chang
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  4. Zhixin Li
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Corresponding author

Correspondence to Huifang Ma.

Additional information

Yang Chang Postgraduate in the College of Computer Science and Engineering, Northwest Normal University, China. Her research interest covers community detection and data mining.

Huifang Ma Professor in the College of Computer Science and Engineering, Northwest Normal University, China. She received her PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2010. Her research interest covers artificial intelligence, data mining, and machine learning. Corresponding author of this paper.

Liang Chang Professor in the School of Computer Science and Information Security, Guilin University of Electronic Technology, China. He received his PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2008. His research interest covers data and knowledge engineering, intelligent recommendation system, and formal methods.

Zhixin Li professor at the College of Computer Science and Information Technology, Guangxi Normal University, China. He obtained his PhD degree in computer software and theory from Institute of Computing Technology, Chinese Academy of Sciences, China in 2010. His research interests include image understanding, machine learning and multimedia information retrieval.

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Chang, Y., Ma, H., Chang, L. et al. Community detection with attributed random walk via seed replacement. Front. Comput. Sci. 16, 165324 (2022). https://doi.org/10.1007/s11704-021-0482-x

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