Article ID Journal Published Year Pages File Type
417785 Computational Statistics & Data Analysis 2010 14 Pages PDF
Abstract

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.

Related Topics
Physical Sciences and Engineering Computer Science Computational Theory and Mathematics
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