IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v11y2005i3d10.1007_s10588-005-5381-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-005-5381-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10588-005-5381-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Michael W. Berry & Murray Browne, 2005. "Email Surveillance Using Non-negative Matrix Factorization," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 249-264, October.
    3. 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.
    4. 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.
    5. Ze Li & Duoyong Sun & Renqi Zhu & Zihan Lin, 2017. "Detecting event-related changes in organizational networks using optimized neural network models," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-21, November.
    6. Sriranga Suprabhath Koduru & Venkata Siva Prasad Machina & Sreedhar Madichetty, 2023. "Cyber Attacks in Cyber-Physical Microgrid Systems: A Comprehensive Review," Energies, MDPI, vol. 16(12), pages 1-36, June.
    7. Ramin Moghaddass & Yongtao Guan, 2022. "Optimal Frameworks for Detecting Anomalies in Sensor-Intensive Heterogeneous Networks," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2583-2610, September.
    8. 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.
    9. Joseph Crawford & Tijana Milenković, 2018. "ClueNet: Clustering a temporal network based on topological similarity rather than denseness," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-25, May.
    10. 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.
    11. Alastair Irons & Harjinder Singh Lallie, 2014. "Digital Forensics to Intelligent Forensics," Future Internet, MDPI, vol. 6(3), pages 1-13, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comaot:v:11:y:2005:i:3:d:10.1007_s10588-005-5381-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.