Finding tribes: Identifying close-knit individuals from employment patterns (2007)
| Venue: | In Proceedings of the 13 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Citations: | 10 - 2 self |
BibTeX
@INPROCEEDINGS{Friedland07findingtribes:,
author = {Lisa Friedland and David Jensen},
title = {Finding tribes: Identifying close-knit individuals from employment patterns},
booktitle = {In Proceedings of the 13 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = {2007}
}
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Abstract
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.
