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What distinguish one from its peers in social networks?

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Abstract

Being able to discover the uniqueness of an individual is a meaningful task in social network analysis. This paper proposes two novel problems in social network analysis: how to identify the uniqueness of a given query vertex, and how to identify a group of vertices that can mutually identify each other. We further propose intuitive yet effective methods to identify the uniqueness identification sets and the mutual identification groups of different properties. We further conduct an extensive experiment on both real and synthetic datasets to demonstrate the effectiveness of our model.

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Acknowledgments

Mi-Yen Yeh’s research is supported in part by National Science Council of Taiwan, R.O.C., under Contracts NSC101-2221-E-001-013. Shou-De Lin’s research is supported by National Science Council, National Taiwan University and Intel Corporation under Grants NSC100-2911-I-002-001, NSC101-2628-E-002-028-MY2 and NTU102R7501. Jian Pei’s research is supported in part by an NSERC Discovery Grant and a BCFRST NRAS Endowment Research Team Program project. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

    Yi-Chen Lo & Shou-De Lin

  2. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Jhao-Yin Li & Mi-Yen Yeh

  3. Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan

    Mi-Yen Yeh

  4. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

    Jian Pei

Authors
  1. Yi-Chen Lo
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  2. Jhao-Yin Li
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  3. Mi-Yen Yeh
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  4. Shou-De Lin
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  5. Jian Pei
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Correspondence to Mi-Yen Yeh.

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Responsible editor: Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezny.

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Lo, YC., Li, JY., Yeh, MY. et al. What distinguish one from its peers in social networks?. Data Min Knowl Disc 27, 396–420 (2013). https://doi.org/10.1007/s10618-013-0330-1

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  • DOI: https://doi.org/10.1007/s10618-013-0330-1

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