Heard, N.A. and Weston, David J. and Platanioti, K. and Hand, D.J. (2010) Bayesian anomaly detection methods for social networks. The Annals of Applied Statistics 4 (2), pp. 645-662. ISSN 1932-6157.
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.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | Dynamic networks, Bayesian inference, counting processes, hurdle models |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Administrator |
| Date Deposited: | 11 Jun 2013 12:55 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/7447 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
