Graph Embedding Framework Based on Adversarial and Random Walk Regularization

Abstract

Graph embedding aims to represent node structural as well as attribute information into a low-dimensional vector space so that some downstream application tasks such as node classification, link prediction, community detection, and recommendation can be easily performed by using simple machine learning algorithms. The graph convolutional network is a neural network framework for machine learning on graphs. Because of its powerful ability to model graph data, it is currently the best choice for graph embedding. However, most existing graph convolutional network-based embedding algorithms not only ignore the data distribution of the latent codes but also lose the high-order proximity between nodes in a graph, leading to inferior embedding. To mitigate this problem, we investigate how to enforce latent codes to match a prior distribution, and we introduce random walk to preserve high-order proximity in a graph. In this paper, we propose a novel graph embedding framework, Adversarial and Random Walk Regularized Graph Embedding (ARWR-GE), which jointly preserves structural and attribute information. ARWR-GE adopts an adversarial training scheme to enforce the latent codes to match a prior distribution, and by employing the skip-gram model, nodes in a random walk sequence are closer in the latent space. We evaluate our proposed framework by using three real-world datasets on link prediction, graph clustering, and visualization tasks. The results demonstrate that our framework achieves better performance than state-of-the-art graph embedding algorithms.