Large‐scale artificial intelligence (AI) models such as ChatGPT have the potential to
improve performance on many benchmarks and real‐world tasks. However, it is difficult to …

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 …

Deep graph neural networks (GNNs) have achieved excellent results on various tasks on
increasingly large graph datasets with millions of nodes and edges. However, memory …

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 …

Despite recent success in using the invariance principle for out-of-distribution (OOD)
generalization on Euclidean data (eg, images), studies on graph data are still limited …

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …

Abstract Physics-Informed Neural Networks (PINNs) and extended PINNs (XPINNs) have
emerged as a promising approach in computational science and engineering for solving …

Physics-informed neural networks (PINNs) have become a popular choice for solving high-
dimensional partial differential equations (PDEs) due to their excellent approximation power …

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 …

Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms
for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical …