Leman Go

Leman Go, a research area focused on improving the Weisfeiler-Leman (WL) test for graph isomorphism, aims to enhance the expressive power of graph neural networks (GNNs) by developing more sophisticated graph representations. Current research explores variations of the WL test, such as loopy and relational extensions, and investigates its relationship to other graph kernel methods like random walk kernels, seeking to improve both theoretical understanding and practical performance in tasks like graph classification and node prediction. These advancements are significant because they directly impact the ability of GNNs to effectively learn from and reason about complex graph-structured data, with implications for various applications including knowledge graph reasoning and molecule analysis.