Email as Spectroscopy: Automated Discovery of Community Structure within Organizations

TL;DR

This paper introduces an automated method using betweenness centrality to identify organizational communities and leadership roles from large-scale email logs, providing insights into formal and informal structures.

Contribution

It presents a novel application of betweenness centrality for community detection in email networks, enabling rapid analysis of large-scale organizational communication data.

Findings

Effective identification of true communities within email networks

Ability to detect leadership roles in organizational communities

Validated results through qualitative evaluation

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.