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Detecting and characterizing dense subgraphs (tight communities) in social and information networks is an important exploratory tool in social network analysis. Several approaches have been proposed that either partition the whole network into "Clusters", even in low density region, or are aimed at finding a single densest community (and need to be iterated to find the next one). As social networks grow larger both approaches and result in algorithms too slow to be practical, in particular when speed in analyzing the data is required. In this paper, the authors propose an approach that aims at balancing efficiency of computation and expressiveness/manageability of the output community representation.
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