Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

The issue of distribution shifts is emerging as a critical concern in graph representation
learning. From the perspective of invariant learning and stable learning, a recently well …

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 …

Abstract Recent studies on Graph Neural Networks (GNNs) provide both empirical and
theoretical evidence supporting their effectiveness in capturing structural patterns on both …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Graph machine learning has been extensively studied in both academia and industry.
Although booming with a vast number of emerging methods and techniques, most of the …

Recent studies have shown that graph neural networks (GNNs) exhibit strong biases
towards the node degree: they usually perform satisfactorily on high-degree nodes with rich …

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

Transfer learning on graphs drawn from varied distributions (domains) is in great demand
across many applications. Emerging methods attempt to learn domain-invariant …

Out-of-distribution (OOD) detection, which aims to identify OOD samples from in-distribution
(ID) ones in test time, has become an essential problem in machine learning. However …