Local flow betweenness centrality for clustering community graphs

Abstract

The problem of information flow is studied to identify de facto communities of practice from tacit knowledge sources that reflect the underlying community structure, using a collection of instant message logs. We characterize and model the community detection problem using a combination of graph theory and ideas of centrality from social network analysis. We propose, validate, and develop a novel algorithm to detect communities based on computation of the Local Flow Betweenness Centrality. Using LFBC, we model the weights on the edges in the graph so we can extract communities. We also present how to compute efficiently LFBC on relevant edges without having to recalculate the measure for each edge in the graph during the process. We validate our algorithms on a corpus of instant messages that we call MLog. Our results demonstrate that MLogs are a useful source for community detection that can augment the study of collaborative behavior. © Springer-Verlag Berlin Heidelberg 2005.