Discovering Important Nodes through Graph Entropy: The Case of Enron Email Database

Discovering Important Nodes through Graph Entropy: The Case of Enron Email Database (2005)

by Jitesh Shetty

Venue:	KDD, Proceedings of the 3rd international workshop on Link discovery
Citations:	15 - 0 self

Datum	Value	Source
TITLE	Discovering Important Nodes through Graph Entropy: The Case of Enron Email Database	INFERENCE
AUTHOR NAME	Jitesh Shetty	SVM HeaderParse 0.2
AUTHOR AFFIL	University of Southern California; University Park	SVM HeaderParse 0.2
AUTHOR ADDR	Los Angeles, CA 90089	SVM HeaderParse 0.2
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.	SVM HeaderParse 0.2
YEAR	2005	INFERENCE
VENUE	KDD, Proceedings of the 3rd international workshop on Link discovery	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	74--81	INFERENCE
CITATIONS	20 found	ParsCit 1.0