An evolutionary autoencoder for dynamic community detection

Abstract

Dynamic community detection is significant for controlling and capturing the temporal features of networks. The evolutionary clustering framework provides a temporal smoothness constraint for simultaneously maximizing the clustering quality at the current time step and minimizing the clustering deviation between two successive time steps. Based on this framework, some existing methods, such as the evolutionary spectral clustering and evolutionary nonnegative matrix factorization, aim to look for the low-dimensional representation by mapping reconstruction. However, such reconstruction does not address the nonlinear characteristics of networks. In this paper, we propose a semi-supervised algorithm (sE-Autoencoder) to overcome the effects of nonlinear property on the low-dimensional representation. Our proposed method extends the typical nonlinear reconstruction model to the dynamic network by constructing a temporal matrix. More specifically, the potential community characteristics and the previous clustering, as the prior information, are incorporated into the loss function as a regularization term. Experimental results on synthetic and real-world datasets demonstrate that the proposed method is effective and superior to other methods for dynamic community detection.