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Statistical inference on attributed random graphs: Fusion of graph features and content

Author

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  • Grothendieck, John
  • Priebe, Carey E.
  • Gorin, Allen L.

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.

Suggested Citation

  • Grothendieck, John & Priebe, Carey E. & Gorin, Allen L., 2010. "Statistical inference on attributed random graphs: Fusion of graph features and content," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1777-1790, July.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:7:p:1777-1790
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    References listed on IDEAS

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    1. Carey E. Priebe & John M. Conroy & David J. Marchette & Youngser Park, 2005. "Scan Statistics on Enron Graphs," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 229-247, October.
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    Cited by:

    1. Priebe, Carey E. & Park, Youngser & Marchette, David J. & Conroy, John M. & Grothendieck, John & Gorin, Allen L., 2010. "Statistical inference on attributed random graphs: Fusion of graph features and content: An experiment on time series of Enron graphs," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1766-1776, July.
    2. N. Lee & C. Priebe, 2011. "A latent process model for time series of attributed random graphs," Statistical Inference for Stochastic Processes, Springer, vol. 14(3), pages 231-253, October.

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