Community-aware dynamic network embedding by using deep autoencoder

Abstract

Network embedding has recently attracted lots of attention due to its wide applications on graph tasks such as link prediction, network reconstruction, node stabilization, and community stabilization, which aims to learn the low-dimensional representations of nodes with essential features. Most existing network embedding methods mainly focus on static or continuous evolution patterns of microscopic node and link structures in networks, while neglecting the dynamics of macroscopic community structures. In this paper, we propose a Community-aware Dynamic Network Embedding method (short for CDNE) which considers the dynamics of macroscopic community structures. First, we model the problem of dynamic network embedding as a minimization of an overall loss function, which tries to maximally preserve the global node structures, local link structures, and continuous community dynamics. Then, we adopt a stacked deep autoencoder algorithm to solve this minimization problem, obtaining the low-dimensional representations of nodes. Extensive experiments on both synthetic networks and real networks demonstrate the superiority of CDNE over the existing methods on tackling various graph tasks.