Robust community detection on dynamic graph

Many approaches have been proposed to identify communities on complex networks. However the current algorithms are sensitive to the variation of input data and parameters. In this paper, we propose a new community detection approach called robust community detection on dynamic network (RCD). The robustness of our algorithm lies in two aspects. Firstly, by adopting the offset of sigmoid function, RCD reduces dependency on the input cluster number. Therefore, RCD is insensitive to the man-made interference and the robustness is guaranteed. Secondly, RCD is not restricted to the type of input networks, because it only depends on the topological structure of network rather than requiring labels or other information of networks. Thus, the application robustness is ensured. RCD are evaluated on both the synthetic and realistic network data. The experiment result shows that by introducing sigmoid function, the error rate of misclassification and iterative times are decreased.