Spectral Graph Neural Networks with Two-dimensional (2-D) Graph Convolution for Improved Graph Learning

The authors propose a novel two-dimensional (2-D) graph convolution paradigm that unifies and generalizes existing spectral graph convolution approaches, enabling error-free construction of arbitrary target outputs.