Computer Science > Machine Learning
arXiv:2603.08825 (cs)
[Submitted on 9 Mar 2026]
Title:Are Expressive Encoders Necessary for Discrete Graph Generation?
View a PDF of the paper titled Are Expressive Encoders Necessary for Discrete Graph Generation?, by Jay Revolinsky and 2 other authors
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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.
| Comments: | |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.08825 [cs.LG] |
| (or arXiv:2603.08825v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08825
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View a PDF of the paper titled Are Expressive Encoders Necessary for Discrete Graph Generation?, by Jay Revolinsky and 2 other authors
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