Detecting Anomalies in Graphs with Numeric Labels

Michael Davis, Weiru Liu, Paul Miller, George Redpath

Research output: Contribution to conferencePaperpeer-review

33 Citations (Scopus)


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 com- bination of structural irregularities and numerical outliers.
Original languageEnglish
Number of pages6
Publication statusPublished - Oct 2011
Event20th ACM Conference on Information and Knowledge Management - Glasgow, United Kingdom
Duration: 24 Oct 201128 Oct 2011


Conference20th ACM Conference on Information and Knowledge Management
Abbreviated titleCIKM 2011
Country/TerritoryUnited Kingdom

Bibliographical note

ISSN: 978-1-4503-0717-8


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