Graph transformers have emerged as a promising architecture for a variety of graph learning
and representation tasks. Despite their successes, though, it remains challenging to scale …

Recently, transformer architectures for graphs emerged as an alternative to established
techniques for machine learning with graphs, such as (message-passing) graph neural …

Node-level random walk has been widely used to improve Graph Neural Networks.
However, there is limited attention to random walk on edge and, more generally, on $ k …

Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts
that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution …

Abstract Graph Transformers (GTs) have proved their advantage in graph-level tasks.
However, existing GTs still perform unsatisfactorily on the node classification task due to 1) …

卷积神经网络被广泛应用于图像识别领域并且展现出强大的特征提取能力,
但它只能处理欧氏空间的结构化数据, 无法适用于非结构化数据的处理. 为应对该限制 …

Graph neural networks (GNNs) have attracted considerable attention from the research
community. It is well established that GNNs are usually roughly divided into spatial and …

Deep learning's performance has been extensively recognized recently. Graph neural
networks (GNNs) are designed to deal with graph-structural data that classical deep …

In recent years, spectral graph neural networks, characterized by polynomial filters, have
garnered increasing attention and have achieved remarkable performance in tasks such as …

The increasing amount of graph data places requirements on the efficiency and scalability of
graph neural networks (GNNs), despite their effectiveness in various graph-related …