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Traditional graph-based clustering methods group vertices into discrete non-intersecting clusters under the assumption that each vertex can belong to only a single cluster. On the other hand, recent research on graph-based clustering methods, applied to real world networks (e.g., Protein-protein interaction networks and social networks), shows overlapping patterns among the underlying clusters. For example, in social networks, an individual is expected to belong to multiple clusters (Or communities), rather than strictly confining himself/herself to just one. As such, overlapping clusters enable better models of real-life phenomena.
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