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Document clustering is a popular topic in data mining and information retrieval. Most models and methods for this problem are based on computing the similarity between pair documents modeled in a space of all terms, or a new feature space obtained by applying a topic modeling technique for a given corpus. In this paper, we regard these two ideas as clustering on term feature and on semantic feature,...
Communities play fundamental organizational and functional roles in various complex network systems. Community detection is an important challenge in network analysis. We approach community detection based on a Shared-Influence-Neighbor (SIN) similarity metric that measures the closeness of a pair of nodes in terms of their mutual influence and the common set of nodes they both influence. In this...
Community detection and influence analysis are significant notions in social networks. We exploit the implicit knowledge of influence-based connectivity and proximity encoded in the network topology, and propose a novel algorithm for both community detection and influence ranking. Using a new influence cascade model, the algorithm generates an influence vector for each node, which captures in detail...
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