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

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 …

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

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

Graph Neural Networks (GNNs) have emerged as powerful representation learning tools for
capturing complex dependencies within diverse graph-structured data. Despite their …

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 …

Abstract Out-of-distribution (OOD) generalization in the graph domain is challenging due to
complex distribution shifts and a lack of environmental contexts. Recent methods attempt to …

Graph machine learning (GML) has been successfully applied across a wide range of tasks.
Nonetheless, GML faces significant challenges in generalizing over out‐of‐distribution …

Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of
recommender systems. Due to its rich semantic content and associations among interactive …

Distribution shift widely exists in graph representation learning and often reduces model
performance. This work investigates how to improve the performance of a graph neural …