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A Probabilistic Approach to Uncovering Attributed Graph Anomalies
[chapter]
2014
Proceedings of the 2014 SIAM International Conference on Data Mining
Uncovering subgraphs with an abnormal distribution of attributes reveals much insight into network behaviors. For example in social or communication networks, diseases or intrusions usually do not propagate uniformly, which makes it critical to find anomalous regions with high concentrations of a specific disease or intrusion. In this paper, we introduce a probabilistic model to identify anomalous subgraphs containing a significantly different percentage of a certain vertex attribute, such as a
doi:10.1137/1.9781611973440.10
dblp:conf/sdm/LiSCGY14
fatcat:svkjuzia5bdathdxnxkokiafsy