prefuse: a toolkit for interactive information visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Computational & Mathematical Organization Theory
Scan Statistics on Enron Graphs
Computational & Mathematical Organization Theory
Structure in the Enron Email Dataset
Computational & Mathematical Organization Theory
Email Surveillance Using Non-negative Matrix Factorization
Computational & Mathematical Organization Theory
Graph Theoretic and Spectral Analysis of Enron Email Data
Computational & Mathematical Organization Theory
The genetic programming collaboration network and its communities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An eigen analysis of the GP community
Genetic Programming and Evolvable Machines
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Network data mining: discovering patterns of interaction between attributes
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Analysing structure in complex networks using quality functions evolved by genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Data Mining and Knowledge Discovery
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During the legal investigation of Enron Corporation, the U.S. Federal Regulatory Commission (FERC) made public a substantial data set of the company's internal corporate emails. This work presents a genetic algorithm (GA) approach to social network analysis (SNA) using the Enron corpus. Three SNA metrics, degree, density, and proximity prestige, were applied to the detection of networks with high email activity and presence of important actors with respect to email transactions. Quantitative analysis revealed that density and proximity prestige captured networks of high activity more so than degree. Subsequent qualitative analysis indicated that there were trade-offs in the selection of SNA metrics. Examination of the discovered social networks showed that density and proximity prestige isolated networks involving key actors to a greater extent than degree. In particular, density picked out interesting patterns in terms of email volume, while proximity prestige better isolated key actors at Enron. The roles of the particular actors picked out by the networks as reasons for their prominence are also discussed.