2010/11/08 by Nicholas A. Heard, David J. Weston, Kiriaki Platanioti +1
Mathematics · #stat.AP
paper · pdf · doi:10.1214/10-aoas329
published as Annals of Applied Statistics 2010, Vol. 4, No. 2, 645-662 · Published in at http://dx.doi.org/10.1214/10-AOAS329 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
arxiv created 2010/11/08 · arxiv updated 2010/11/09
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.