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Graph Theoretic and Spectral Analysis of Enron Email Data

Author

Listed:
  • Anurat Chapanond

    (Department of Computer Science Rensselaer Polytechnic Institute)

  • Mukkai S. Krishnamoorthy

    (Department of Computer Science Rensselaer Polytechnic Institute)

  • Bülent Yener

    (Department of Computer Science Rensselaer Polytechnic Institute)

Abstract

Analysis of social networks to identify communities and model their evolution has been an active area of recent research. This paper analyzes the Enron email data set to discover structures within the organization. The analysis is based on constructing an email graph and studying its properties with both graph theoretical and spectral analysis techniques. The graph theoretical analysis includes the computation of several graph metrics such as degree distribution, average distance ratio, clustering coefficient and compactness over the email graph. The spectral analysis shows that the email adjacency matrix has a rank-2 approximation. It is shown that preprocessing of data has significant impact on the results, thus a standard form is needed for establishing a benchmark data.

Suggested Citation

  • Anurat Chapanond & Mukkai S. Krishnamoorthy & Bülent Yener, 2005. "Graph Theoretic and Spectral Analysis of Enron Email Data," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 265-281, October.
    • Handle: RePEc:spr:comaot:v:11:y:2005:i:3:d:10.1007_s10588-005-5381-4
    DOI: 10.1007/s10588-005-5381-4
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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. P. S. Keila & D. B. Skillicorn, 2005. "Structure in the Enron Email Dataset," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 183-199, October.
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    Cited by:

    1. Hady W. Lauw & Ee-Peng Lim & HweeHwa Pang & Teck-Tim Tan, 2005. "Social Network Discovery by Mining Spatio-Temporal Events," Computational and Mathematical Organization Theory, Springer, vol. 11(2), pages 97-118, July.
    2. Uddin, Shahadat & Murshed, Shahriar Tanvir Hasan & Hossain, Liaquat, 2011. "Power-law behavior in complex organizational communication networks during crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(15), pages 2845-2853.
    3. Jana Diesner & Terrill L. Frantz & Kathleen M. Carley, 2005. "Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different”," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 201-228, October.
    4. Danica Vukadinović Greetham & Zhivko Stoyanov & Peter Grindrod, 2014. "On the radius of centrality in evolving communication networks," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 540-560, October.

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