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.
social network, spatio-temporal data mining, link analysis
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Workshop on Link Analysis, Counterterrorism and Security, at the SIAM International Conference on Data Mining 2008, April 23
City or Country
LAUW, Hady Wirawan; LIM, Ee Peng; TAN, Teck Tim; and PANG, Hwee Hwa.
Mining Social Network from Spatio-Temporal Events. (2005). Workshop on Link Analysis, Counterterrorism and Security, at the SIAM International Conference on Data Mining 2008, April 23. 82-93. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1138
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