Learning continuous embeddings for graph categories instead of using fixed one-hot vectors improves molecular graph generation quality, as shown by EGF's superior performance on standard benchmarks.
This paper introduces Embedded Graph Flows (EGF), a generative model that creates realistic molecular graphs by learning continuous embeddings for node and edge types instead of using fixed one-hot vectors. The model uses a permutation-equivariant transformer to gradually transform noise into valid graph structures, achieving state-of-the-art results on molecular benchmarks like QM9 and ZINC250k.