Are Expressive Encoders Necessary for Discrete Graph Generation?

• URL: http://arxiv.org/abs/2603.08825v1
• Date: Mon, 09 Mar 2026 18:36:06 GMT
• Title: Are Expressive Encoders Necessary for Discrete Graph Generation?
• Abstract summary: We introduce GenGNN, a modular message-passing framework for graph generation.<n>Diffusion models with GenGNN achieve more than 90% validity on Tree and Planar datasets.<n>For molecule generation, DiGress with a GenGNN backbone achieves 99.49% Validity.
• Score: 40.00511566138393
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Discrete graph generation has emerged as a powerful paradigm for modeling graph data, often relying on highly expressive neural backbones such as transformers or higher-order architectures. We revisit this design choice by introducing GenGNN, a modular message-passing framework for graph generation. Diffusion models with GenGNN achieve more than 90% validity on Tree and Planar datasets, within margins of graph transformers, at 2-5x faster inference speed. For molecule generation, DiGress with a GenGNN backbone achieves 99.49% Validity. A systematic ablation study shows the benefit provided by each GenGNN component, indicating the need for residual connections to mitigate oversmoothing on complicated graph-structure. Through scaling analyses, we apply a principled metric-space view to investigate learned diffusion representations and uncover whether GNNs can be expressive neural backbones for discrete diffusion.

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