The success of deep learning has been widely recognized in many machine learning tasks during the last decades, ranging from image classificationand speech recognitionto 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 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. Further analysis establishes a strong link across the spatial and spectral domains  .
Deep learning's success has been widely recognized in a variety of machine learning tasks , including image classification, audio recognition, and natural language processing . As an extension of deep learning beyond these domains, graph neural networks (GNNs) are designed to handle the non-Euclidean graph-structure which is intractable to previous deep learning techniques. 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, as well as expose the connections within each domain. A review of spectral graph theory and approximation theory builds a strong relationship across the spatial and spectral domains in further investigation .
R
The success of deep learning
Deep learning's success
R
many
a variety of
R
during the last decades, ranging from image classificationand speech recognitionto natural language understanding
, including image classification, audio recognition, and natural language processing
R
, Graph
beyond these domains, graph
R
solve
handle
R
problems on graph-structured data which can hardly be handled by general
graph-structure which is intractable to previous
R
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
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
R
and reveal connections among subcategories in
as well as expose the connections within
R
Further analysis establishes a strong link
A review of spectral graph theory and approximation theory builds a strong relationship
A
in further investigation