While deep learning has achieved great success on various tasks, the task-specific model
training notoriously relies on a large volume of labeled data. Recently, a new training …

Recently," pre-training and fine-tuning''has been adopted as a standard workflow for many
graph tasks since it can take general graph knowledge to relieve the lack of graph …

Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration
with graph data, a cornerstone in our interconnected world, remains nascent. This paper …

Emerging as fundamental building blocks for diverse artificial intelligence applications,
foundation models have achieved notable success across natural language processing and …

Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are
prominent techniques for homogeneous and heterogeneous graph representation learning …

Recommender systems commonly suffer from the long-standing data sparsity problem
where insufficient user-item interaction data limits the systems' ability to make accurate …

Few-shot Node Classification (FSNC) is a challenge in graph representation learning where
only a few labeled nodes per class are available for training. To tackle this issue, meta …

Graph learning has become indispensable for interpreting and harnessing relational data in
diverse fields, ranging from recommendation systems to social network analysis. In this …

Fine-tuning pre-trained models has recently yielded remarkable performance gains in graph
neural networks (GNNs). In addition to pre-training techniques, inspired by the latest work in …

Recently, much exertion has been paid to design graph self-supervised methods to obtain
generalized pre-trained models, and adapt pre-trained models onto downstream tasks …