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The typical task of unsupervised learning is to organize data, for example into clusters, typically disjoint clusters (eg. the K-means algorithm). One would expect (for example) a clustering of books into topics to present overlapping clusters. The situation is even more so in social networks, a source of ever increasing data. Finding the groups or communities in social networks based on interactions...
Most of the existing social network systems require from their users an explicit statement of their friendship relations. In this paper we focus on implicit communities of Web users and present an approach to automatically detect such communities based on user's resource manipulations. This approach is dynamic as user groups appear and evolve along with users interests over time. Moreover, new resources...
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