  Deep learning's performance has been extensively recognized recently. Graph
neural networks (GNNs) are designed to deal with graph-structural data that
classical deep learning does not easily manage. Since most GNNs were created
using distinct theories, direct comparisons are impossible. Prior research has
primarily concentrated on categorizing existing models, with little attention
paid to their intrinsic connections. The purpose of this study is to establish
a unified framework that integrates GNNs based on spectral graph and
approximation theory. The framework incorporates a strong integration between
spatial- and spectral-based GNNs while tightly associating approaches that
exist within each respective domain.
