Graph-based approaches to insider threat detection
Proceedings of the 5th Annual Workshop on Cyber Security and Information Intelligence Research: Cyber Security and Information Intelligence Challenges and Strategies
Finding the k-Most Abnormal Subgraphs from a Single Graph
DS '09 Proceedings of the 12th International Conference on Discovery Science
Personalized privacy protection in social networks
Proceedings of the VLDB Endowment
Finding top-k shortest path distance changes in an evolutionary network
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
OddBall: spotting anomalies in weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
SigSpot: mining significant anomalous regions from time-evolving networks (abstract only)
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
MultiAspectForensics: mining large heterogeneous networks using tensor
International Journal of Web Engineering and Technology
Autonomously reviewing and validating the knowledge base of a never-ending learning system
Proceedings of the 22nd international conference on World Wide Web companion
Discovery of extreme events-related communities in contrasting groups of physical system networks
Data Mining and Knowledge Discovery
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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.