Identification of overlapping community structure with Grey Relational Analysis in social networks

Community structure is a very important characteristic of complex networks, detecting communities within networks has very important significance in several disciplines like computer science, physics, biology, etc. To some extent, Realworld networks exhibit overlapping community structure. To solve this problem, we devise a novel algorithm to identify overlapping communities in social networks with Grey Relational Analysis. This paper presents the edge vector which is a measure of relationships among nodes, and uses balanced closeness degree to describe edge similarity, computes edge clusters and finally obtains overlapping community structure. The effectiveness and the efficiency of the new algorithm is evaluated by experiments on both real-world and the computer-generated datasets.