Temporal graphs serve as a powerful framework for representing networks whose connections evolve over time. By incorporating time‐stamped interactions, these models capture the dynamic nature of ...

Graph neural networks (GNNs) have rapidly emerged as a central methodology for analysing complex datasets presented as graphs, where entities are interconnected through diverse relationships. By ...

Multi-electrode arrays (MEAs) provide a noninvasive interface with sub-millisecond temporal resolution and long-term, multi-site recordings, enabling ...

The team proposed propose a novel entity-type-enriched cascaded neural network (E 2 CNN) that considers the overlap triple problem and entity-type information to construct a Chinese financial ...