GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hubs, authorities, and communities
ACM Computing Surveys (CSUR)
Hyperlink Analysis for the Web
IEEE Internet Computing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A Fast Algorithm to Find Overlapping Communities in Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Discovering Knowledge-Sharing Communities in Question-Answering Forums
ACM Transactions on Knowledge Discovery from Data (TKDD)
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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.