Accurate, scalable in-network identification of p2p traffic using application signatures
Proceedings of the 13th international conference on World Wide Web
Scan Statistics on Enron Graphs
Computational & Mathematical Organization Theory
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
Computational Statistics & Data Analysis
Betti numbers of graphs with an application to anomaly detection
Statistical Analysis and Data Mining
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Many problems can be cast as statistical inference on an attributed random graph. Our motivation is change detection in communication graphs. We prove that tests based on a fusion of graph-derived and content-derived metadata can be more powerful than those based on graph or content features alone. For some basic attributed random graph models, we derive fusion tests from the likelihood ratio. We describe the regions in parameter space where the fusion improves power, using both numeric results from selected small examples and analytic results on asymptotically large graphs.