Abstract: In the field of graph self-supervised learning (GSSL), graph autoencoders and graph contrastive learning are two mainstream methods. Graph autoencoders aim to learn representations by ...

Google published details of a new kind of AI based on graphs called a Graph Foundation Model (GFM) that generalizes to previously unseen graphs and delivers a three to forty times boost in precision ...

The integration of electronic medical records (EMRs) in modern healthcare holds significant promise; however, traditional approaches to syndrome differentiation in Traditional Chinese Medicine (TCM) ...

Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart and plays a crucial role in diagnosing heart disease and assessing cardiac function. In the context of ...

Abstract: Graph neural networks (GNNs) encounter challenges in establishing deep structures and managing a large number of parameters effectively to learn node features comprehensively. Consequently, ...