Clenshaw Graph Neural Networks

Abstract

Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational spatial methods for learning graph representations. Polynomial filters, which have an advantage on heterophilous graphs, are motivated differently from the spectral perspective of graph convolutions. Recent spatial GCN models use various residual connection techniques to alleviate the model degradation problem such as over-smoothing and gradient vanishing. However, current residual connections do not effectively harness the full potential of polynomial filters, which are commonly employed in the spectral domain of GNNs.

Topics & Concepts