Before revision:  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 GNNsinto 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.
After revision:  Existing GNNs are presented using various techniques, making direct comparison and cross-reference more complex. Although existing studies categorize GNNs into spatial-based and spectral-based techniques, there hasn't been a thorough examination of their relationship. To close this gap, this study presents a single framework that systematically incorporates most GNNs. URLanize existing GNNs into spatial and spectral domains, and reveal connections among subcategories in each domain.