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

@article{Priebe2010StatisticalIO,
  title={Statistical inference on attributed random graphs: Fusion of graph features and content: An experiment on time series of Enron graphs},
  author={C. Priebe and Youngser Park and D. Marchette and John M. Conroy and John Grothendieck and A. Gorin},
  journal={Comput. Stat. Data Anal.},
  year={2010},
  volume={54},
  pages={1766-1776}
}
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. 
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