Compressed Representation of Web and Social Networks via Dense Subgraphs

Mining and analyzing large web and social networks are challenging tasks in terms of storage and information access. In order to address this problem, several works have proposed compressing large graphs allowing neighbor access over their compressed representations. In this paper, we propose a novel compressed structure aiming to reduce storage and support efficient navigation over web and social graph compressed representations. Our approach uses clustering and mining for finding dense subgraphs and represents them using compact data structures. We perform experiments using a wide range of web and social networks and compare our results with the best known techniques. Our results show that we improve the state of the art space/time tradeoffs for supporting neighbor queries. Our compressed structure also enables mining queries based on dense subgraphs, such as cliques and bicliques.

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