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Given a large online network of online auction users and their histories of transactions, how can we spot anomalies and auction fraud? This paper describes the design and implementation of NetProbe, a system that we propose for solving this problem. NetProbe models auction users and transactions as a Markov Random Field tuned to detect the suspicious patterns that fraudsters create, and employs a Belief Propagation mechanism to detect likely fraudsters. Our experiments show that NetProbe isdoi:10.1145/1242572.1242600 dblp:conf/www/PanditCWF07 fatcat:o2dxptvcd5dkfauehzpsi5lrde