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Cross-domain community detection in heterogeneous social networks

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Abstract

With the recent surge of location-based social networks (LBSNs), e.g., Foursquare and Facebook Places, huge amount of human digital footprints that people leave in the cyber-physical space become accessible, including users’ profiles, online social connections, and especially the places that they have checked in. Different from social networks (e.g., Flickr, Facebook) which have explicit groups for users to subscribe or join, LBSNs usually have no explicit community structure. Meanwhile, unlike social networks which only contain a single type of social interaction, the coexistence of online/offline social interactions and user/venue attributes in LBSNs makes the community detection problem much more challenging. In order to capitalize on the large number of potential users/venues as well as the huge amount of heterogeneous social interactions, quality community detection approach is needed. In this paper, by exploring the heterogenous digital footprints of LBSNs users in the cyber-physical space, we come out with a novel edge-centric co-clustering framework to discover overlapping communities. By employing inter-mode as well as intra-mode features, the proposed framework is able to group like-minded users from different social perspectives. The efficacy of our approach is validated by intensive empirical evaluations based on the collected Foursquare dataset.

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Acknowledgments

This work is partially supported by the National Basic Research Program of China (No. 2012CB-316400), the EU FP7 Project SOCIETIES (No. 257493), the National Natural Science Foundation of China (No. 61222209, 61103063), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20126102110043), the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2012JQ8028), the Scholarship Award for Excellent Doctoral Student Granted by Ministry of Education of China, and the Doctorate Foundation of Northwestern Polytechnical University (No. CX201018). The authors would like to thank all the colleagues for their discussion and suggestion. Zhu WANG would like to thank the China Scholarship Council (CSC) for his joint PhD funding.

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072, China

    Zhu Wang & Xingshe Zhou

  2. Department of Telecommunication Network and Services, Institut Mines-TELECOM/TELECOM SudParis, 91011, Evry, France

    Daqing Zhang, Dingqi Yang & Zhiyong Yu

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  1. Zhu Wang
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  2. Xingshe Zhou
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  3. Daqing Zhang
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  4. Dingqi Yang
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  5. Zhiyong Yu
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Correspondence to Zhu Wang.

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Wang, Z., Zhou, X., Zhang, D. et al. Cross-domain community detection in heterogeneous social networks. Pers Ubiquit Comput 18, 369–383 (2014). https://doi.org/10.1007/s00779-013-0656-0

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