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Statistical inference on attributed random graphs: Fusion of graph features and content: An experiment on time series of Enron graphs

Author

Listed:
  • Priebe, Carey E.
  • Park, Youngser
  • Marchette, David J.
  • Conroy, John M.
  • Grothendieck, John
  • Gorin, Allen L.

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.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:7:p:1766-1776
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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.
    2. 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.
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    Cited by:

    1. 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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