Email as Spectroscopy: Automated Discovery of Community Structure within Organizations

Abstract

We describe a methodology for the automatic identification of communities of practice from email logs within an organization. We use a betweeness centrality algorithm that can rapidly find communities within a graph representing information flows. We apply this algorithm to an email corpus of nearly one million messages collected over a two-month span, and show that the method is effective at identifying true communities, both formal and informal, within these scale-free graphs. This approach also enables the identification of leadership roles within the communities. These studies are complemented by a qualitative evaluation of the results in the field.

Cited by in corpus (10)

• The clustering coefficient and community structure of bipartite networks
• Accuracy and Precision of Methods for Community Identification in Weighted Networks
• Shortest Path Discovery in the Multi-layered Social Network
• How to search a social network
• Modelling Selforganization and Innovation Processes in Networks
• An Introduction to Community Detection in Multi-layered Social Network
• Empirical Analysis and Evolving Model of Bipartite Networks
• Statistical Network Topology for Crisis Informetrics
• Analysis of Social Group Dynamics
• Group Evolution Discovery in Social Networks