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June 2010 Bayesian anomaly detection methods for social networks
Nicholas A. Heard, David J. Weston, Kiriaki Platanioti, David J. Hand
Ann. Appl. Stat. 4(2): 645-662 (June 2010). DOI: 10.1214/10-AOAS329


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.


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Nicholas A. Heard. David J. Weston. Kiriaki Platanioti. David J. Hand. "Bayesian anomaly detection methods for social networks." Ann. Appl. Stat. 4 (2) 645 - 662, June 2010.


Published: June 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1194.62021
MathSciNet: MR2758643
Digital Object Identifier: 10.1214/10-AOAS329

Keywords: Bayesian inference , counting processes , Dynamic networks , hurdle models

Rights: Copyright © 2010 Institute of Mathematical Statistics


Vol.4 • No. 2 • June 2010
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