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 …

In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Recognizing causal relationships embedded within data is pivotal for a comprehensive …

The training and inference of Graph Neural Networks (GNNs) are costly when scaling up to
large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate …

Graph representation learning on vast datasets, like web data, has made significant strides.
However, the associated computational and storage overheads raise concerns. In sight of …

In the realm of deep learning-based recommendation systems, the increasing computational
demands, driven by the growing number of users and items, pose a significant challenge to …

Graph learning plays a pivotal role and has gained significant attention in various
application scenarios, from social network analysis to recommendation systems, for its …

Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and …

Graph out-of-distribution (OOD) generalization remains a major challenge in graph learning
since graph neural networks (GNNs) often suffer from severe performance degradation …

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 …