Adversarial learning based residual variational graph normalized autoencoder for network representation

Abstract

With the success of Graph Neural Networks (GNNs) on non-Euclidean data, some GNN-based approaches to network representation learning have emerged in recent years. Graph autoencoder (GAE) and variational GAE (VGAE) are unsupervised learning frameworks widely used to learn latent embedding representations based on graph-structured data. However, it performs poorly in link prediction, node clustering, and graph visualization task when isolated nodes are involved or when the raw data representation is weak. Based on the perspective of improving the capability of the latent embedding representation and the robustness of the model, we propose an Adversarial Learning based Residual Variational Graph Normalized Autoencoder (ARVGNA). We introduce a residual network to augment the raw data representation, use L2-normalization to derive better node embeddings, and add adversarial learning to promote the activation of the node representation and enhance the generalization of the model. The experiments on three benchmark datasets show that the proposed ARVGNA greatly outperforms the baselines in the tasks of link prediction, node clustering, and graph visualization, and exhibits strong competitive power and high robustness in network representation.