Dense Subgraph Extraction with Application to Community Detection

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Dense Subgraph Extraction with Application to Community Detection
— This paper presents a method for identifying a set of dense subgraphs of a given sparse graph. Within the main applications of this “dense subgraph problem”, the dense subgraphs are interpreted as communities, as in, e.g., social networks. The problem of identifying dense subgraphs helps analyze graph structures and complex networks and it is known to be challenging. It bears some similarities with the problem of reordering/blocking matrices in sparse matrix techniques. We exploit this link and adapt the idea of recognizing matrix column similarities, in order to compute a partial clustering of the vertices in a graph, where each cluster represents a dense subgraph. In contrast to existing subgraph extraction techniques which are based on a complete clustering of the graph nodes, the proposed algorithm takes into account the fact that not every participating node in the network needs to belong to a community. Another advantage is that the method does not require to specify the ...
Jie Chen 0007, Yousef Saad
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where TKDE
Authors Jie Chen 0007, Yousef Saad
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