With the deep understanding of the time-varying characteristics of real systems, research studies focusing on the temporal network spring up like mushrooms. Community detection is an accompanying and meaningful problem in the temporal network, but the analysis of this problem is still in its developing stage. In this paper, we proposed a temporal spectral clustering method to detect the invariable communities in the temporal network. Through integrating Fiedler’s eigenvectors of normalized Laplacian matrices within a limited time window, our method can avoid the inaccurate partition caused by the mutation of the temporal network. Experiments demonstrated that our model is effective in solving this problem and performs obviously better than the compared methods. The results illustrated that taking the historical information of the network structure into consideration is beneficial in clustering the temporal network.
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