Most of the previous research on community detection are mainly based on the undirected graph structures. However, in actual complex networks, the links relation usually shows the asymmetric characteristic or directionality, such as citation network of scientific papers, the one-way follow relationship on Twitter, and hyperlinks between web pages. Therefore, based on the propagation of informat ion and the direction of information transmission, a k-Path conception and calculation method for measuring the similarity of co-community neighboring is presented to weigh possibility of nodes in the same community. Furthermore, the method of transferring directed graphs into undirected graphs with similarity of weight is presented. Then the local extension algorithm of detecting overlapping community based on weighted undirected graphs is proposed. Several experiments on the real data sets are conducted and analyzed. Experimental results demonstrate that the k-Path conception can achieve the reasonable conversion for directed graph and improve the effectiveness of the community gathering nodes. Finally, the results show that the algorithm can detect the overlapping community effectively.