oddball: Spotting Anomalies in Weighted Graphs

Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nodes. The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ranks and eigenvalues that seem to govern the socalled “neighborhood sub-graphs” and we show how to use these rules for anomaly detection; (b) we carefully choose features, and design OddBall, so that it is scalable and it can work un-supervised (no user-defined constants) and (c) we report experiments on many real graphs with up to

• Type: Conference
• Year: 2010
• Where: PAKDD
• Authors: Leman Akoglu, Mary McGlohon, Christos Faloutsos
• Keywords: Anomaly Detection, Data Mining, OddBall Algorithm, PAKDD 2010, Weighted Graph