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Graphs are widely used in large scale social network analysis now-a-days. Not only analysts need to focus on cohesive sub-graphs to study patterns among social actors, but also normal users are interested in discovering what happening in their neighborhood. However, effectively storing large scale social network and efficiently identifying cohesive sub-graphs is challenging. In this paper, the authors introduce a novel sub-graph concept to capture the cohesion in social interactions, and propose an I/O efficient approach to discover cohesive sub-graphs.
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