Enhancing community detection by using local structural information

Abstract

Many real-world networks, such as gene networks, protein–protein interaction networks and metabolic networks, exhibit community structures, meaning the existence of groups of densely connected vertices in the networks. Many local similarity measures in the networks are closely related to the concept of the community structures, and may have a positive effect on community detection in the networks. Here, various local similarity measures are used to extract local structural information, which is then applied to community detection in the networks by using the edge-reweighting strategy. The effect of the local similarity measures on community detection is carefully investigated and compared in various networks. The experimental results show that the local similarity measures are crucial for the improvement of community detection methods, while the positive effect of the local similarity measures is closely related to the networks under study and applied community detection methods.