The rapid growth of graph data poses significant challenges in storage, transmission, and
particularly the training of graph neural networks (GNNs). To address these challenges …

Graph condensation (GC) is an emerging technique designed to learn a significantly smaller
graph that retains the essential information of the original graph. This condensed graph has …

Graph distillation (GD) is an effective approach to extract useful information from large-scale
network structures. However, existing methods, which operate in Euclidean space to …

Dataset condensation has significantly improved model training efficiency, but its application
on devices with different computing power brings new requirements for different data sizes …

Graph neural networks (GNNs) have achieved significant success in many real-world
applications by performing message-passing between nodes to embed graph data into low …

Out-of-distribution (OOD) generalization issue is a well-known challenge within deep
learning tasks. In dynamic graphs, the change of temporal environments is regarded as the …