document: Netprobe : a fast and scalable system for fraud detection in online auction networks 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 is both efficient and effective for fraud detection . We report experiments on synthetic graphs with as many as 7,000 nodes and 30,000 edges , where NetProbe was able to spot fraudulent nodes with over 90 % precision and recall , within a matter of seconds . We also report experiments on a real dataset crawled from eBay , with nearly 700,000 transactions between more than 66,000 users , where NetProbe was highly effective at unearthing hidden networks of fraudsters , within a realistic response time of about 6 minutes . For scenarios where the underlying data is dynamic in nature , we propose IncrementalNetProbe , which is an approximate , but fast , variant of NetProbe . Our experiments prove that Incremental NetProbe executes nearly doubly fast as compared to NetProbe , while retaining over 99 % of its accuracy .
doc_bio_tags: O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O