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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.

We present a method for creating a network of gene co-occurrences from the literature and partitioning it into communities of related genes. The way in which our method identifies communities makes it likely that the component genes of each community will be related by their function. The method processes a large database of article abstracts, synthesizing information from many sources to shed light on groups of genes that have been shown to interact. It is a tool to be used by researchers in the biomedical sciences to swiftly search for known interactions and to provide insight into unexplored connections. The partitioning procedure is designed to be particularly applicable to large networks in which individual nodes may play a role in more than one community. In this paper, we explain the details of the method, in particular the partitioning process. We also apply the method to produce communities of genes related to colon cancer and show that the results are useful.