Mining frequent correlated-Quasi-Cliques from PPI networks

Many of the previous studies show convincing arguments that mining frequent subgraphs is especially useful. Many hidden frequent patterns which are very interesting can not be found by mining single graph. Previous studies as Quasi-Clique have little success with the hub problem. In this paper, we introduce a new conception Correlated-Quasi-Clique and develop a novel algorithm, CoClique, to address the hub problem and improve the efficiency of frequent correlated-Quasi-Cliques mining. Meanwhile, we exploit several effective techniques to prune the search space. An extensive experimental evaluation on real databases demonstrates that our algorithm outperforms previous methods.