The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Researcher affiliation extraction from homepages
NLPIR4DL '09 Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries
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The analysis of graphs like collaborative networks aims at studying the relationships between individuals, instead of individual attributes or properties. One of the interesting substructures of such a graph is a community structure, which is a subset of nodes that are more densely linked when compared with the rest of the network. Such dense subgraphs gather individuals who share similar interests depending on the type of relation encoded in the graph. In this paper we tackle the problem of identifying communities in dynamic networks. We propose an approach based on the random walk to identify communities in evolving graphs like collaborative networks. We apply this approach to the Infocom co-authorship network to determine stable collaborations and evolving communities. We use such information, combined with other Digital Bibliography & Library Project (DBLP) co-authorship network topology features, to analyse the formation of the programme committee board of a conference.