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 …

Many real-world datasets can be naturally represented as graphs, spanning a wide range of
domains. However, the increasing complexity and size of graph datasets present significant …

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

Graph condensation (GC) has recently garnered considerable attention due to its ability to
reduce large-scale graph datasets while preserving their essential properties. The core …

The increasing prevalence of large-scale graphs poses a significant challenge for graph
neural network training, attributed to their substantial computational requirements. In …

In graph classification, the out-of-distribution (OOD) issue is attracting great attention. To
address this issue, a prevailing idea is to learn stable features, on the assumption that they …

Recently, graph condensation has emerged as a prevalent technique to improve the training
efficiency for graph neural networks (GNNs). It condenses a large graph into a small one …

Invariant learning demonstrates substantial potential for enhancing the generalization of
graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize …

Training recommendation models on large datasets requires significant time and resources.
It is desired to construct concise yet informative datasets for efficient training. Recent …

Despite Graph Neural Networks (GNNs) demonstrating considerable promise in graph
representation learning tasks, GNNs predominantly face significant issues with overfitting …