Discovering Structural Anomalies in Graph-Based Data

The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, particularly for fraud, but less work has been done in terms of detecting anomalies in graph-based data. While there has been some work that has used statistical metrics and conditional entropy measurements, the results have been limited to certain types of anomalies and specific domains. In this paper we present graph- based approaches to uncovering anomalies in domains where the anomalies consist of unexpected entity/relationship deviations that resemble non- anomalous behavior. Using synthetic and real-world data, we evaluate the effectiveness of these algorithms at discovering anomalies in a graph-based representation of data.