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 1.6 million nodes, where OddBall indeed spots unusual nodes that agree with intuition.

DOI: 10.1007/978-3-642-13672-6_40

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@inproceedings{Akoglu2010oddballSA, title={oddball: Spotting Anomalies in Weighted Graphs}, author={Leman Akoglu and Mary McGlohon and Christos Faloutsos}, booktitle={PAKDD}, year={2010} }