Discovering correlated spatio-temporal changes in evolving graphs
Knowledge and Information Systems
Public vs. private: comparing public social network information with email
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Integrating multiple document features in language models for expert finding
Knowledge and Information Systems
Improving customer retention in financial services using kinship network information
Expert Systems with Applications: An International Journal
Clustering social networks using interaction semantics and sentics
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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We initiate the study of a new clustering framework, called cluster ranking. Rather than simply partitioning a network into clusters, a cluster ranking algorithm also orders the clusters by their strength. To this end, we introduce a novel strength measure for clusters—the integrated cohesion—which is applicable to arbitrary weighted networks. We then present a new cluster ranking algorithm, called C-Rank. We provide extensive theoretical and empirical analysis of C-Rank and show that it is likely to have high precision and recall. A main component of C-Rank is a heuristic algorithm for finding sparse vertex separators. At the core of this algorithm is a new connection between vertex betweenness and multicommodity flow. Our experiments focus on mining mailbox networks. A mailbox network is an egocentric social network, consisting of contacts with whom an individual exchanges email. Edges between contacts represent the frequency of their co–occurrence on message headers. C-Rank is well suited to mine such networks, since they are abundant with overlapping communities of highly variable strengths. We demonstrate the effectiveness of C-Rank on the Enron data set, consisting of 130 mailbox networks.