Mining maximal subnetworks from interaction network with node attributes

Abstract

Detecting densely connected subgraphs is of great importance in sociology, biology and computer science disciplines where systems are often represented as a large graph. Several approaches have been proposed for detecting dense connected subgraphs in large graphs. Often these large graphs have additional attribute data characterizing either the nodes or edges of a graph. Recent research has combined the problem of dense connected subgraph detection with subspace similarity over attribute data. While detecting dense and cohesive subgraphs is desirable, the density factor can prevent the existing algorithms from reporting highly cohesive subgraphs which are not particularly dense. In this paper, we introduce an algorithm for mining maximal cohesive subgraphs from node attributed graphs. Unlike other approaches for detecting dense subgraphs, this algorithm does not require any density threshold. It discovers all maximal cohesive subgraphs regardless of their density. Experiments on real world datasets show that the proposed approach is effective in mining meaningful biological subgraphs from protein-protein interaction network, where attributes are extracted from gene expression datasets. We compare the proposed approach with the baseline technique, and results show that the proposed algorithm is much faster than the baseline algorithm.