Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks

Abstract

The success of deep learning has been widely recognized in many machine learning tasks during the last decades, ranging from image classification and speech recognition to natural language understanding. As an extension of deep learning, Graph neural networks (GNNs) are designed to solve the non-Euclidean problems on graph-structured data which can hardly be handled by general deep learning techniques. Existing GNNs under various mechanisms, such as random walk, PageRank, graph convolution, and heat diffusion, are designed for different types of graphs and problems, which makes it difficult to compare them directly. Previous GNN surveys focus on categorizing current models into independent groups, lacking analysis regarding their internal connection. This paper proposes a unified framework and provides a novel perspective that can widely fit existing GNNs into our framework methodologically. Specifically, we survey and categorize existing GNN models into the spatial and spectral domains, and reveal connections among subcategories in each domain. Further analysis establishes a strong link across the spatial and spectral domains.