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Towards multi-dimensional knowledge-aware approach for effective community detection in LBSN

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Abstract

In this paper, we focus on the problem of detecting communities, where users have similar characteristics in both social relationship and check-in behavior in location based social network (LBSN). Contrast to traditional social network, LBSN not only contains users’ online social relationship information but also large amounts of location information generated by users’ check-in behavior, which inevitably brings challenges to community detection in LBSN. To do this, based on abundant knowledge hidden in LBSN, we first define multiple kinds of knowledge-aware similarities as well as corresponding calculation methods. Then, we propose a method called as Multi-dimensional Similarity Information Fusion for Community Detection (MFCD) on the basis of an improved K-Means algorithm. Meanwhile, we establish a set of evaluation metrics to measure community quality from different perspectives, specifically for LBSN. Finally, we conduct a series of experiments to demonstrate the excellent performance of our proposed community detection method for LBSN.

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The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

This paper is partially supported by National Natural Science Foundation of China (No. 62076224).

Funding

This paper is partially supported by National Natural Science Foundation of China (No. 62076224) and Hubei Natural Science Foundation of China (No. 2019CFA023).

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430078, China

    Dazhao Xu & Yunliang Chen

  2. School of Computer Science and Technology, AnHui University, Hefei, 230601, China

    Ningning Cui

  3. School of Information Technology, Deakin University, Geelong, VIC3220, Australia

    Jianxin Li

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  1. Dazhao Xu
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  2. Yunliang Chen
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Contributions

Dazhao Xu wrote the manuscript and Yunliang Chen contributed to the conception of the study, Ningning Cui and Jianxin Li contributed significantly to preparation and modification of manuscript. All authors reviewed the manuscript.

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Correspondence to Yunliang Chen.

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Xu, D., Chen, Y., Cui, N. et al. Towards multi-dimensional knowledge-aware approach for effective community detection in LBSN. World Wide Web 26, 1435–1458 (2023). https://doi.org/10.1007/s11280-022-01101-7

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