In this paper, a new community detection approach combing the evidential modularity, spectral mapping and evidential c-means is presented to identify the overlapping graph structure in complex networks. Although many overlapping community-detection algorithms have been developed before, most of them are based on fuzzy partitions. Credal partitions, in the frame of belief function theory, have many advantages compared with fuzzy ones and enable us to have a better insight into the **data** structure. As shown in the experimental results for two networks in the real world, credal partitions can reflect our degree of uncertain more intuitively. Actually, the credal partition is an extension of both hard and fuzzy ones, thus there is no doubt that more rich information of the graph structure could be available from the detected structure by the method proposed here. We expect that the evidential clustering approaches will be employed with promising results in the detection of overlapping communities in complex networks with practical significance.
