Heterophilous graphs, where dissimilar nodes tend to connect, pose a challenge for graph
neural networks (GNNs) as their superior performance typically comes from aggregating …

Graph Neural Networks (GNNs) have gained significant attention in recent years due to their
ability to learn representations of graph structured data. Two common methods for training …

Recently, graph neural network (GNN) has emerged as a powerful representation learning
tool for graph-structured data. However, most approaches are tailored for undirected graphs …

With the recent advancements in graph neural networks (GNNs), spectral GNNs have
received increasing popularity by virtue of their specialty in capturing graph signals in the …

The $ q $-parameterized magnetic Laplacian serves as the foundation of directed graph
(digraph) convolution, enabling this kind of digraph neural network (MagDG) to encode …

Dynamic graphs (DGs), which capture time-evolving relationships between graph entities,
have widespread real-world applications. To efficiently encode DGs for downstream tasks …

Machine unlearning, as a pivotal technology for enhancing model robustness and data
privacy, has garnered significant attention in prevalent web mining applications, especially …

The directed graph (digraph), as a generalization of undirected graphs, exhibits superior
representation capability in modeling complex topology systems and has garnered …