Structural Role Enhanced Attributed Network Embedding

Abstract

In recent years, network embedding methods based on deep learning to process network structure data have attracted widespread attention. It aims to represent nodes in the network as low-dimensional dense real-value vectors and effectively preserve network structure and other valuable information. Most network embedding methods now only preserve the network topology and do not take advantage of the rich attribute information in networks. In this paper, we propose a novel deep attributed network embedding framework (RolEANE), which can preserve network topological structure and attribute information well at the same time. The framework consists of two parts, one of which is the network structural role proximity enhanced deep autoencoder, which is used to capture highly nonlinear network topological structure and attribute information. The other part is that we proposed a neighbor optimization strategy to modify the Skip-Gram model so that it can integrate the network topological structure and attribute information to improve the final embedded performance. The experiments on four real datasets show that our method outperforms other state-of-the-art network embedding methods.