Design Your Own Universe: A Physics-Informed Agnostic Method for
Enhancing Graph Neural Networks
Design Your Own Universe: A Physics-Informed Agnostic Method for
Enhancing Graph Neural Networks
Physics-informed Graph Neural Networks have achieved remarkable performance in learning through graph-structured data by mitigating common GNN challenges such as over-smoothing, over-squashing, and heterophily adaption. Despite these advancements, the development of a simple yet effective paradigm that appropriately integrates previous methods for handling all these challenges is still underway. In …