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Social Network Discovery by Mining Spatio-Temporal Events

Author

Listed:
  • Hady W. Lauw

    (Nanyang Technological University)

  • Ee-Peng Lim

    (Nanyang Technological University)

  • HweeHwa Pang

    (Singapore Management University)

  • Teck-Tim Tan

    (Nanyang Technological University)

Abstract

Knowing patterns of relationship in a social network is very useful for law enforcement agencies to investigate collaborations among criminals, for businesses to exploit relationships to sell products, or for individuals who wish to network with others. After all, it is not just what you know, but also whom you know, that matters. However, finding out who is related to whom on a large scale is a complex problem. Asking every single individual would be impractical, given the huge number of individuals and the changing dynamics of relationships. Recent advancement in technology has allowed more data about activities of individuals to be collected. Such data may be mined to reveal associations between these individuals. Specifically, we focus on data having space and time elements, such as logs of people's movement over various locations or of their Internet activities at various cyber locations. Reasoning that individuals who are frequently found together are likely to be associated with each other, we mine from the data instances where several actors co-occur in space and time, presumably due to an underlying interaction. We call these spatio-temporal co-occurrences events, which we use to establish relationships between pairs of individuals. In this paper, we propose a model for constructing a social network from events, and provide an algorithm that mines these events from the data. Experiments on a real-life data tracking people's accesses to cyber locations have also yielded encouraging results.

Suggested Citation

  • Hady W. Lauw & Ee-Peng Lim & HweeHwa Pang & Teck-Tim Tan, 2005. "Social Network Discovery by Mining Spatio-Temporal Events," Computational and Mathematical Organization Theory, Springer, vol. 11(2), pages 97-118, July.
    • Handle: RePEc:spr:comaot:v:11:y:2005:i:2:d:10.1007_s10588-005-3939-9
    DOI: 10.1007/s10588-005-3939-9
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    References listed on IDEAS

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    1. P. S. Keila & D. B. Skillicorn, 2005. "Structure in the Enron Email Dataset," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 183-199, October.
    2. Anurat Chapanond & Mukkai S. Krishnamoorthy & Bülent Yener, 2005. "Graph Theoretic and Spectral Analysis of Enron Email Data," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 265-281, October.
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