Bayesian anomaly detection methods for social networks. (English) Zbl 1194.62021
Summary: 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.
MSC:
| 62F15 | Bayesian inference |
| 91D30 | Social networks; opinion dynamics |
| 05C90 | Applications of graph theory |
| 65C60 | Computational problems in statistics (MSC2010) |
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