Expressive Graph Neural Network

Expressive Graph Neural Networks (GNNs) aim to improve the ability of GNNs to accurately represent and learn from complex graph structures. Current research focuses on developing GNN architectures that go beyond the limitations of the Weisfeiler-Lehman test by incorporating features like cycle representations (e.g., via message detouring) and leveraging homomorphism counting to achieve a more fine-grained understanding of their expressive power. This pursuit of enhanced expressiveness is driven by the need for improved performance in various graph-related tasks, such as graph classification, node classification, and graph generation, particularly in domains like bioinformatics and drug discovery. The development of more expressive and efficient GNNs promises significant advancements in these fields.