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Detecting and characterizing dense subgraphs (tight communities) in socialand information networks is an important exploratory tool in social networkanalysis. Several approaches have been proposed that either (i) partition thewhole network into clusters, even in low density region, or (ii) are aimed atfinding a single densest community (and need to be iterated to find the nextone). As social networks grow larger both approaches (i) and (ii) result inalgorithms too slow to be practical, in particular when speed in analyzing thedata is required. In this paper we propose an approach that aims at balancingefficiency of computation and expressiveness and manageability of the outputcommunity representation. We define the notion of a partial dense cover (PDC)of a graph. Intuitively a PDC of a graph is a collection of sets of nodes that(a) each set forms a disjoint dense induced subgraphs and (b) its removalleaves the residual graph without dense regions. Exact computation of PDC is anNP-complete problem, thus, we propose an efficient heuristic algorithms forcomputing a PDC which we christen Core and Peel. Moreover we propose a novelbenchmarking technique that allows us to evaluate algorithms for computing PDCusing the classical IR concepts of precision and recall even without a goldenstandard. Tests on 25 social and technological networks from the Stanford LargeNetwork Dataset Collection confirm that Core and Peel is efficient and attainsvery high precison and recall.

Detecting dense communities in large social and information networks with the Core & Peel algorithm