Frequent coherent subgraphs can provide valuable knowledge about the underlying internal structure of a graph database, and mining frequently occurring coherent subgraphs from large dense graph databases has been witnessed several applications and received considerable attention in the graph mining community recently. In this paper, we study how to efficiently mine the complete set of coherent closed quasi-cliques from large dense graph databases, which is an especially challenging task due to the downward-closure property no longer holds. By fully exploring some properties of quasicliques, we propose several novel optimization techniques, which can prune the unpromising and redundant sub-search spaces effectively. Meanwhile, we devise an efficient closure checking scheme to facilitate the discovery of only closed quasi-cliques. We also develop a coherent closed quasi-clique mining algorithm, Cocain 1 . Thorough performance study shows that Cocain is very efficient and scalable for large dense graph databases.
In a preliminary version of this work REF ], we studied a more general problem formulation, that is, mining frequent closed quasi-cliques from large dense graph databases, and designed an efficient closed coherent quasiclique discovery algorithm, Cocain.
Coherent closed quasi-clique discovery from large dense graph databases
KDD '06
Computer Science