Bayesian anomaly detection methods for social networks
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)
 |
Edit/View Item |