Ant Colony Clustering Approach Combined with Multilevel Framework for Functional Module Detection in Large-Scale PPI Networks

Abstract

Swarm intelligence algorithms have been successfully applied to the detection of functional modules in PPI networks. As the increasing of the PPI network size, those algorithms will cost more time in functional module detection. In this paper, we present a novel algorithm, ACC-MLF, which combines ant colony clustering with multilevel framework to reduce the runtime in the large-scale PPI networks. First, use a new matching strategy to coarsen the original large-scale PPI network, and get a smaller PPI network. Then, use the ant colony clustering algorithm to cluster the obtained network. Finally, get the clustering result of original network through de-coarsening and use the refinement to avoid the result from falling into the local optimal. Experiments in some large-scale networks show that the detecting speed of ACC-MLF has significantly improved in contrast to ACC-FMD, and ACC-MLF can get better clustering results in some evaluation metrics while compared with ACC-FMD, MCODE, MINE and Core algorithms.