Community Detection in Networks with Less Significant Community Structure
Label propagation is a low complexity approach to community detection in complex networks. Research has extended the basic label propagation algorithm (LPA) in multiple directions including maximizing the modularity, a well known quality function to evaluate the goodness of a community division, of the detected communities. Current state of the art modularity specialized label propagation algorithm (LPAm+ maximizes modularity using a two stage iterative procedure: the first stage is to assign labels to nodes using label propagation, the second stage merges smaller communities to further improve modularity. LPAm+ has been shown able to achieve excellent performance on networks with significant community structure where the network modularity is above a certain threshold. However, we show in this paper that for networks with less significant community structure, LPAm+ tends to get trapped in local optimal solutions that are far from optimal. The main reason comes from the fact that the first stage of LPAm+ often misplaces node labels and severely hinders the merging operation in the second stage. We overcome the drawback of LPAm+ by correcting the node labels after the first stage. We apply a label propagation procedure inspired by the meta heuristic Record to Record Travel algorithm that reassigns node labels to improve modularity before merging communities. Experimental results show that the proposed algorithm, named meta LPAm+ outperforms LPAm+ in terms of modularity on networks with less significant community structure while retaining almost the same performance on networks with significant community structure.
ADMA