1 WL Expressiveness
1-WL expressiveness refers to the ability of graph neural networks (GNNs) to distinguish between non-isomorphic graphs, a crucial aspect of their performance. Current research focuses on understanding the practical limitations of this expressiveness, particularly comparing the theoretical capabilities of models like those based on the Weisfeiler-Lehman test with their actual performance on real-world datasets, and exploring methods to enhance expressiveness in GNNs and other architectures such as polynomial neural networks, while addressing scalability challenges. This research is significant because it helps determine whether increased theoretical expressiveness translates to improved performance in practical applications and guides the development of more efficient and powerful graph-based machine learning models.