Abstract：Community structure is one of the important topological characteristics in complex networks. In real world,community structures in networks are often overlapped. And it is difficult to efficiently detect overlapping communities in a network. Optimizing Qov function directly is a solution for overlapping community detection,however,it is easy to generate a local optimal solution. To solve this problem,the concept of vertex central membership measure is introduced,and based on cluster prototypes of nodes in a network,an efficient framework is proposed to identify overlapping communities. Then the framework is applied to some classic clustering algorithms. The experimental results show that the proposed method avoids generating local optimal solution,and it is more efficient than the other algorithms on synthetic and real-world networks.
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