Automated Discoveryof Community Structure within Organizations英文资料.pdf

Email as Spectroscopy: Automated Discovery of Community Structure within Organizations Joshua R. Tyler, Dennis M. Wilkinson, Bernardo A. Huberman HP Labs, 1501 Page Mill Road, Palo Alto CA, 94304 {jtyler; dennisw; huberman}@ Abstract. We describe a methodology for the automatic identification of communities of practice from email logs within an organization. We use a betweenness 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. Introduction Email has become the predominant means of communication in the information society. It pervades business, social and technical exchanges and as such it is a highly relevant area for research on communities and social networks. Not surprisingly, email has been established as an indicator of collaboration and knowledge exchange [Wellman, 2002, Whittaker Sidner, 1996]. Email is also a tantalizing medium for research because it provides plentiful data on personal communication in an electronic form. This volume of data enables the discovery of shared interests and relationships where none were previously known [Schwartz Wood, 1992]. Given its ubiquity, it is a promising resource for tapping into the dynamics of information within organizations, and for extracting the hidden patterns of collaboration and leadership that are at the heart of communities of practice. Communities