Invariant graph representation learning aims to learn the invariance among data from
different environments for out-of-distribution generalization on graphs. As the graph …

The burgeoning volume of graph data presents significant computational challenges in
training graph neural networks (GNNs), critically impeding their efficiency in various …

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 …

The majority of existing heterogeneous graph neural network algorithms (HGNNs) rely on
meta-paths to capture the substantial semantic information present in heterogeneous graphs …

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

Out-of-distribution (OOD) generalization has gained increasing attentions for learning on
graphs, as graph neural networks (GNNs) often exhibit performance degradation with …

Graph neural networks (GNNs) have exhibited remarkable performance under the
assumption that test data comes from the same distribution of training data. However, in real …

Recent studies have highlighted fairness issues in Graph Neural Networks (GNNs), where
they produce discriminatory predictions against specific protected groups categorized by …

Graph Neural Networks (GNNs) have become essential in interpreting relational data across
various domains, yet, they often struggle to generalize to unseen graph data that differs …

Although graph neural networks have achieved great success in the task of molecular
property prediction in recent years, their generalization ability under out-of-distribution …