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Incorporating profile information in community detection for online social networks

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  • Fan, W.
  • Yeung, K.H.

Abstract

Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users’ profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

Suggested Citation

  • Fan, W. & Yeung, K.H., 2014. "Incorporating profile information in community detection for online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 226-234.
    • Handle: RePEc:eee:phsmap:v:405:y:2014:i:c:p:226-234
    DOI: 10.1016/j.physa.2014.02.075
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    References listed on IDEAS

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    1. Fan, W. & Yeung, K.H. & Wong, K.Y., 2013. "Assembly effect of groups in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1090-1099.
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    Cited by:

    1. Wu, Jianshe & Hou, Yunting & Jiao, Yang & Li, Yong & Li, Xiaoxiao & Jiao, Licheng, 2015. "Density shrinking algorithm for community detection with path based similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 218-228.

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