Embedded Graph Flows for Categorical Graph Generation

2026-09-04

AI Summary

为了解决生成分类图时节点和边类型选择的问题，提出Embedded Graph Flows模型，通过学习连续嵌入并使用图变换器将高斯噪声转换为目标嵌入，以生成更合理的图结构。

Abstract

Generating categorical graphs requires choosing node and edge types that form a coherent structure without depending on node order. Many graph generators encode categories as fixed one-hot vectors, which can impose an artificial geometry in which categories are equidistant. We propose Embedded Graph Flows (EGF), a generative model that learns continuous embeddings for node and unordered-edge categories and transports Gaussian noise towards these learnt endpoints using a permutation-equivariant graph transformer. A terminal readout maps the embeddings back to discrete graph categories. Across molecular benchmarks, EGF achieved competitive performance. On QM9, EGF gives the best result on all four reported metrics among the three methods, including a Fréchet ChemNet Distance (FCD) of 0.150, compared with 0.717 for the categorical-diffusion baseline DiGress and 0.812 for the bridge-based baseline GruM. When applied to larger molecules in ZINC250k, EGF retains the lowest maximum mean discrepancy (MMD) using the neighbourhood subgraph pairwise distance kernel (NSPDK), indicating close agreement with the local substructures of the reference molecules. Our code is available at https://github.com/Trusted-System-Lab/EGF.

Problem

• categorical graphs
• node and edge types
• one-hot vectors
• artificial geometry

Innovation

• Embedded Graph Flows
• permutation-equivariant graph transformer
• continuous embeddings
• categorical graphs generation