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PAKDD
2010
ACM

oddball: Spotting Anomalies in Weighted Graphs

14 years 4 months ago
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
Leman Akoglu, Mary McGlohon, Christos Faloutsos
Added 20 Jul 2010
Updated 20 Jul 2010
Type Conference
Year 2010
Where PAKDD
Authors Leman Akoglu, Mary McGlohon, Christos Faloutsos
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