Mining representative maximal dense cohesive subnetworks
Massive amounts of graph data have been generated in many areas, including computational biology and social networks. Often these graphs have attributes associated with nodes. One of the most intriguing questions in graphs representing complex data is to find communities or clusters. The use of attribute data in finding clusters is shown to be effective in many application areas, e.g., finding subnetwork biomarkers for cancer prediction and targeted advertising for a group of friends in social network. In this paper, we propose an algorithm for mining maximal dense cohesive clusters from node-attributed graphs. Typically the number of reported maximal dense cohesive clusters can be very large for relaxed constraints; therefore, we propose a post-processing algorithm for extracting a representative subset of these clusters. Experiments on real-world datasets show that the proposed approach is effective in mining meaningful biological clusters from proteinprotein interaction network with attributes extracted from gene expression datasets. Furthermore, the proposed approach outperforms competitive algorithms in terms of the running time of the algorithm.
interaction networks biological complexes dense subnetworks reverse search