Graph Neural Networks (GNNs) have emerged as a versatile framework for learning from graph-structured data by propagating and aggregating node and edge features along the topology. Methodological ...

A new conditioned diffusion-based graph attention network from the University of Naples Parthenope delivers probabilistic ...

Integrating diverse biomedical modalities is essential for robust healthcare insights, and graph-based models are increasingly used to capture complex relational structures. Yet, their clinical ...

A new hybrid architecture combining graph convolutions with a lite capsule network delivers an average 9.1 percent ...