Discovering Important Nodes through Graph Entropy: The Case of Enron Email Database (2005)
| Venue: | KDD, Proceedings of the 3rd international workshop on Link discovery |
| Citations: | 15 - 0 self |
BibTeX
@INPROCEEDINGS{Shetty05discoveringimportant,
author = {Jitesh Shetty},
title = {Discovering Important Nodes through Graph Entropy: The Case of Enron Email Database},
booktitle = {KDD, Proceedings of the 3rd international workshop on Link discovery},
year = {2005},
pages = {74--81},
publisher = {Press}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
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.
