Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
417784 | Computational Statistics & Data Analysis | 2010 | 11 Pages |
Abstract
Fusion of information from graph features and content can provide superior inference for an anomaly detection task, compared to the corresponding content-only or graph feature-only statistics. In this paper, we design and execute an experiment on a time series of attributed graphs extracted from the Enron email corpus which demonstrates the benefit of fusion. The experiment is based on injecting a controlled anomaly into the real data and measuring its detectability.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Carey E. Priebe, Youngser Park, David J. Marchette, John M. Conroy, John Grothendieck, Allen L. Gorin,