Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

The" Graph pre-training and fine-tuning" paradigm has significantly improved Graph Neural
Networks (GNNs) by capturing general knowledge without manual annotations for …

In recent years, deep learning on graphs has achieved remarkable success in various
domains. However, the reliance on annotated graph data remains a significant bottleneck …

Graph neural networks (GNNs) are emerging for machine learning research on graph-
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …

The integration of multiple heterogeneous data into graph models has been the subject of
extensive research in recent years. Harnessing these resulting Heterogeneous Information …

The question-answering system for Life science research, which is characterized by the
rapid pace of discovery, evolving insights, and complex interactions among knowledge …

Traffic flow forecasting is a fundamental research issue for transportation planning and
management, which serves as a canonical and typical example of spatial-temporal …

Despite the recent success of Message-passing Graph Neural Networks (MP-GNNs), the
strong inductive bias of homophily limits their ability to generalize to heterophilic graphs and …

Current studies have proposed the incorporation of kernel methods with graph
representation learning, and graph kernels have attracted widespread attention for …

Researching genes and their interactions is crucial for deciphering the fundamental laws of
cellular activity, advancing disease treatment, drug discovery, and more. Large language …