Abstract Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) based on
graph signal filters. Some models able to learn arbitrary spectral filters have emerged …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Designing spectral convolutional networks is a challenging problem in graph learning.
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …

We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs
to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical …

The graph Transformer emerges as a new architecture and has shown superior
performance on various graph mining tasks. In this work, we observe that existing graph …

Graph neural networks (GNNs) have achieved great success in many graph-based
applications. However, the enormous size and high sparsity level of graphs hinder their …

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined
polynomial basis and learn the coefficients from the training data. It has been observed that …

Abstract In Graph Neural Networks (GNNs), hierarchical pooling operators generate local
summaries of the data by coarsening the graph structure and the vertex features …

Abstract Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art
method for graph-based learning tasks. However, training GCNs at scale is still challenging …