document: 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 .
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