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  1. Proceedings of the 3rd international workshop on Link discovery (LinkKDD '05)
  2. Discovering important nodes through graph entropy the case of Enron email database
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Bayes net graphs to understand co-authorship networks?
EventRank: a framework for ranking time-varying networks
Graph building as a mining activity: finding links in the small
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The political blogosphere and the 2004 U.S. election: divided they blog
Capital and benefit in social networks
Email alias detection using social network analysis
Mining hidden community in heterogeneous social networks
GiveALink: mining a semantic network of bookmarks for web search and recommendation
Discovering important nodes through graph entropy the case of Enron email database
A latent mixed membership model for relational data
Discovering missing links in Wikipedia

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Discovering important nodes through graph entropy the case of Enron email database

Content Provider ACM Digital Library
Author Adibi, Jafar Shetty, Jitesh
Abstract A major problem in social network analysis and link discovery is the discovery of hidden organizational structure and selection of interesting influential members based on low-level, incomplete and noisy evidence data. To address such a challenge, we exploit an information theoretic model that combines information theory with statistical techniques from area of text mining and natural language processing. The Entropy model identifies the most interesting and important nodes in a graph. We show how entropy models on graphs are relevant to study of information flow in an organization. We review the results of two different experiments which are based on entropy models. The first version of this model has been successfully tested and evaluated on the Enron email dataset.
Starting Page 74
Ending Page 81
Page Count 8
File Format PDF
ISBN 1595932151
DOI 10.1145/1134271.1134282
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2005-08-21
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Entropy Graph theory Link discovery
Content Type Text
Resource Type Article
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