Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …

Graph neural networks (GNNs) encounter significant computational challenges when
handling large-scale graphs, which severely restricts their efficacy across diverse …

Graph Neural Networks (GNNs) have attracted tremendous attention by demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

Spatio-temporal graph neural networks (STGNN) have become the most popular solution to
traffic forecasting. While successful, they rely on the message passing scheme of GNNs to …

Generative self-supervised learning on graphs, particularly graph masked autoencoders,
has emerged as a popular learning paradigm and demonstrated its efficacy in handling non …

Knowledge distillation (KD), as an effective compression technology, is used to reduce the
resource consumption of graph neural networks (GNNs) and facilitate their deployment on …

Distilling high-accuracy Graph Neural Networks~(GNNs) to low-latency multilayer
perceptrons~(MLPs) on graph tasks has become a hot research topic. However, MLPs rely …

Graph neural networks (GNNs) are the dominant paradigm for classifying nodes in a graph,
but they have several undesirable attributes stemming from their message passing …

In terms of accuracy, Graph Neural Networks (GNNs) are the best architectural choice for the
node classification task. Their drawback in real-world deployment is the latency that …

Graph neural networks (GNNs) have been extensively employed in node classification.
Nevertheless, recent studies indicate that GNNs are vulnerable to topological perturbations …