Date of Original Version

6-2010

Type

Conference Proceeding

Rights Management

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-13672-6_40

Abstract or Description

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 so-called “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 millionnodes, where oddball indeed spots unusual nodes that agree with intuition.

DOI

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

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Published In

Lecture Notes in Computer Science, 6119, 410-421.