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2007

Mining for Structural Anomalies in Graph-based Data

11 years 6 months ago
Mining for Structural Anomalies in Graph-based Data
—In this paper we present graph-based approaches to mining for anomalies in domains where the anomalies consist of unexpected entity/relationship alterations that closely resemble non-anomalous behavior. We introduce three novel algorithms for the purpose of detecting anomalies in all possible types of graph changes. Each of our algorithms focuses on a specific graph change and uses the minimum description length principle to discover those substructure instances that contain anomalous entities and relationships. Using synthetic and real-world data, we evaluate the effectiveness of each of these algorithms in terms of each of the types of anomalies. Each of these algorithms demonstrates the usefulness of examining a graph-based representation of data for the purposes of detecting fraud.
William Eberle, Lawrence B. Holder
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where DMIN
Authors William Eberle, Lawrence B. Holder
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