Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

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 Foundation Model (GFM) is a new trending research topic in the graph domain,
aiming to develop a graph model capable of generalizing across different graphs and tasks …

Data imbalance is easily found in annotated data when the observations of certain
continuous label values are difficult to collect for regression tasks. When they come to …

Despite the recent progress of molecular representation learning, its effectiveness is
assumed on the close-world assumptions that training and testing graphs are from identical …

Attribute reconstruction is used to predict node or edge features in the pre-training of graph
neural networks. Given a large number of molecules, they learn to capture structural …

Predicting the resilience of complex networks, which represents the ability to retain
fundamental functionality amidst external perturbations or internal failures, plays a critical …

Graph rationales are representative subgraph structures that best explain and support the
graph neural network (GNN) predictions. Graph rationalization involves the joint …

Inverse molecular design with diffusion models holds great potential for advancements in
material and drug discovery. Despite success in unconditional molecule generation …