Detecting anomalies in graphs with numeric labels

This paper presents Yagada, an algorithm to search labelled graphs for anomalies using both structural data and numeric attributes. Yagada is explained using several security-related examples and validated with experiments on a physical Access Control database. Quantitative analysis shows that in the upper range of anomaly thresholds, Yagada detects twice as many anomalies as the best-performing numeric discretization algorithm. Qualitative evaluation shows that the detected anomalies are meaningful, representing a combination of structural irregularities and numerical outliers. Keywords graph mining, transaction graphs, anomaly detection

• Type: Journal
• Year: 2011
• Where: CIKM
• Authors: Michael Davis, Weiru Liu, Paul Miller, George Redpath
• Keywords: Anomaly Detection, CIKM 2011, Control Database, Information Technology, Numeric Attributes