Bayesian anomaly detection methods for social networks
Nicholas A. Heard, David J. Weston, Kiriaki Platanioti, David J. Hand
The Annals of Applied Statistics · 2010 · Volume 4 · Issue 2
Abstract
Learning the network structure of a large graph is computationally demanding, and dynamically monitoring the network over time for any changes in structure threatens to be more challenging still. This paper presents a two-stage method for anomaly detection in dynamic graphs: the first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior; the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes. The utility of the method is demonstrated on simulated and real data sets.
Keywords
Bayesian inference, counting processes, Dynamic networks, hurdle models
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