Graph Based Neural

Graph-based neural networks (GBNNs) leverage the power of graph representations to model complex relationships within data, enabling the development of powerful predictive models across diverse scientific domains. Current research focuses on adapting GBNNs to various tasks, including robotic manipulation, fluid dynamics simulation, and medical image analysis, often employing graph convolutional networks (GCNs) and message-passing neural networks (MPNNs) as core architectures. This approach offers advantages in handling unstructured data, capturing spatial-temporal dependencies, and improving model efficiency and interpretability, leading to advancements in fields ranging from materials science to healthcare. The ability of GBNNs to effectively model complex interactions makes them a valuable tool for tackling challenging problems across numerous scientific disciplines.

Papers