  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. We organize
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.
