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Power-law distributions of attributes in community detection

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Abstract

Community detection has drawn significant attention as new media generates big data every day. To provide statistical testing procedures for community detection in social networks, a scanning method has been developed based on the likelihood of Poisson random graph. However, the scan statistics did not consider detecting communities of the attributes with power-law distribution. Power-law distribution, generally followed by network attributes, is conspicuous in many scientific situations. This paper aims at extending the scanning method to analyze a social network in which attributes follow power-law distribution. Besides the theoretical construction, simulation studies are performed to verify the feasibility of the proposed method, and an authorship network is used to demonstrate the proposed method.

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Acknowledgments

This work was supported by (a) Career Development Award of Academia Sinica (Taiwan) grant number 103-CDA-M04 and National Science Council (Taiwan) grant number 102-2628-M-001-002-MY3, for Phoa, (b) Thematic Research Program of Academia Sinica (Taiwan) grant number AS-103-TP-C03 for Phoa and Wang.

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Authors and Affiliations

  1. Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

    Tai-Chi Wang & Frederick Kin Hing Phoa

  2. Department of Statistics, National Chengchi University, Taipei, Taiwan

    Tun-Chieh Hsu

Authors
  1. Tai-Chi Wang
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  2. Frederick Kin Hing Phoa
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  3. Tun-Chieh Hsu
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Correspondence to Tai-Chi Wang.

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Wang, TC., Phoa, F.K.H. & Hsu, TC. Power-law distributions of attributes in community detection. Soc. Netw. Anal. Min. 5, 45 (2015). https://doi.org/10.1007/s13278-015-0283-z

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  • DOI: https://doi.org/10.1007/s13278-015-0283-z

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