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2007
ACM

Finding tribes: identifying close-knit individuals from employment patterns

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Finding tribes: identifying close-knit individuals from employment patterns
We present a family of algorithms to uncover tribes--groups of individuals who share unusual sequences of affiliations. While much work inferring community structure describes large-scale trends, we instead search for small groups of tightly linked individuals who behave anomalously with respect to those trends. We apply the algorithms to a large temporal and relational data set consisting of millions of employment records from the National Association of Securities Dealers. The resulting tribes contain individuals at higher risk for fraud, are homogenous with respect to risk scores, and are geographically mobile, all at significant levels compared to random or to other sets of individuals who share affiliations. Categories and Subject Descriptors D.2.8 [Database Management]: Database Applications ? Data mining; I.5.1 [Pattern Recognition]: Models ? Statistical; J.4 [Social and Behavioral Sciences]. General Terms Algorithms, Performance, Design. Keywords Social networks, dynamic netwo...
Lisa Friedland, David Jensen
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2007
Where KDD
Authors Lisa Friedland, David Jensen
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