Attention based adversarially regularized learning for network embedding

Abstract

Network embedding, also known as graph embedding and network representation learning, is an effective method for representing graphs or network data in a low-dimensional space. Most existing methods focus on preserving network topology and minimizing the reconstruction errors to learn a low-dimensional embedding vector representation of the network. In addition, some researchers are devoted to the embedding learning of attribute networks. These researchers usually study the two matrices of network structure and network attributes separately, and then merge them to realize the embedding learning representation of attribute networks. These studies have different performances on a variety of downstream tasks. However, most of these methods have two problems: first, these methods mostly use shallow model to learn structure or attribute embedding, which do not make full use of the rich information contained in the network, such as the neighborhood information of nodes; second, the distribution of the learned network low-dimensional vector representation is overlooked, which leads to poor generalization ability of the model in some real-world network data. Therefore, this paper proposes an adversarially regularized network representation learning model based on attention mechanism, which encodes the topology features and content information of the network into a low-dimensional embedding vector representation through a graph attention autoencoder. Meanwhile, through an adversarial training schema, the learned low-dimensional vector representation could circumvent the requirement of an explicit prior distribution, and thus obtain better generalization ability. Extensive experiments on tasks of link prediction and node clustering demonstrate the effectiveness of learned network embeddings.