Abstract: Detection of overlapping communities in a complex network is a general problem in data mining of network data sets.In view of the phenomenon of a lot of overlapping communities in real networks,we devise the novel algorithm of detection of overlapping community based on Fuzzy c-means clustering.Further more,we propose the improved modularity to evaluate the overlapping community structure based on NG modularity.Firstly,network nodes were mapped into the nodes in Euclidean space.Secondly,the nodes were clustered by Fuzzy c-means algorithm.Thirdly,the best overlapping community was selected by computing the function of improved modularity.Finally,the developed algorithm has been tested on two common real-world networks.Computational results demonstrate that the improved modularity and the algorithm have better partitioning ability and lower time complexity.