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NeurIPS 2026CornellCornell Tech, CornellGraph generation

Regularization Paths for Continuous DAG Learning

Diyang Li, Fei Wang, Kyra Gan

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U California, IrvineUC IrvineGraph generation

xVGAE: A Hierarchical Variational Graph Autoencoder for Exchangeable Graphs

Daniele Micheletti, Federica Zoe Ricci, Erik Sudderth

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U AlbertaGraph generation

EDISCO: Equivariant Discrete Diffusion for Euclidean Combinatorial Optimization

Ruogu Chen, Jie Han

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026State University of New York at National Institutes of HealthPOSTECHUniversity at Buffalo, SUNYGraph generation

MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation

Protyay Dey, Ayush Roy, Hyuna Cho, Won Hwa Kim and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026WuhanNational University of SingaporeRenmin University of ChinaTsinghuaU California, San DiegoGraph generation

E²Gen: Evidential Energy-Based Generation for Fair Federated Graph Learning

Jingbo Wang, Zitong Shi, Yuxin Wu, Zihan Tan and 5 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Seoul NationalGraph generation

SNACK: A Sequential Notation Framework for Probabilistic Graph Generation

Hohyun Kim, Hyesung Kim, Min-hwan Oh, Seunggeun Lee

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026School of Informatics, UniversitU EdinburghGraph generation

Gradient-based Graph Structure Optimisation for Research Networks

Louis Mahon, Mirella Lapata

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026U PennsylvaniaAalto University / IIT BombayMIT —> Aalto University/YaiYaGraph generation

Exact Topological Compliance: Generating Persistence-Equivalent Graphs

Mattie Ji, Indradyumna Roy, Vikas Garg

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026ShanxiU ManchesterShanxi universityGraph generation

Mixed-Curvature Geometric Latent Diffusion Model for Graph Generation

Minxue Qi, Xian Yang, Liang Bai

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026Florida InternationalGraph generation

Towards Fair Graph Generation Without Sensitive Attribute

Zichong Wang, Zhipeng Yin, Wenbin Zhang

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026Sun Yat-senSUN YAT-SEN UNIVERSITYGraph generation

RGF: Recursive Generative Framework for the Edge-Cut Separable Problems

Bo Peng, Rongzhen Ye, Yaohua Li, Weilin Luo and 3 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026AppleVecML Inc.Arizona StateGraph generation

Robust Graph Diffusion Model

Yancheng Wang, Ping Li, Dongfang Sun, Yingzhen Yang

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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NeurIPS 2026U TorontoGraph generation

Literati: Towards Anytime Optimal Shape Generalized Trees via AO*

Nakul Upadhya, Eldan Cohen

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Rey Juan CarlosEPFL - EPF LausanneEPFLU PennsylvaniaGraph generation

DiPhon: Diffusion on Graphons for Scalable Graph Generation

DiPhon defines graphon diffusion via a Jacobi SDE for scalable graph generation, matching first moments exactly and preserving topology across sizes without retraining.

Sergio Rozada, Yiming QIN, Manuel Madeira, Pascal Frossard and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 4/10
strict 1/5
71%Highly rated
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NeurIPS 2026U OxfordMax Planck Institute for MultidiInstitue for Advanced Study, PriMax PlancInstitute for MultidiscOxfordGraph generation

Permutation-Invariant Spectral Learning via Dyson Diffusion

Dyson Diffusion Model uses Dyson Brownian motion to shift inductive bias to diffusion dynamics, yielding permutation-invariant spectral learning that improves graph generation.

Tassilo Schwarz, Cai Dieball, Constantin Kogler, Renaud Lambiotte and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 6 of 20 reviewers recommend it
lenient 2/5
medium 4/10
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NeurIPS 2026Institute of Computing TechnologNorthwesternResearch Center Of Distributed SU the Chinese Academy of ScienceInstitute of Computing TechnologGraph generation

GraDE: A Graph Diffusion Estimator for Frequent Subgraph Discovery in Neural Architectures

GraDE uses a graph diffusion estimator to score subgraph typicality and discovers large-scale neural architecture motifs with up to 30x higher median frequency than sampling methods.

Yikang Yang, Zhengxin Yang, Minghao Luo, Luzhou Peng and 4 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 7 of 20 reviewers recommend it
lenient 3/5
medium 4/10
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80%Must read
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NeurIPS 2026KAISTLG AI ResearchKAIST AISeoul NationalGraph generation

MolHIT: Advancing Molecular-Graph Generation with Hierarchical Discrete Diffusion Models

MolHIT uses hierarchical discrete diffusion and decoupled atom encoding to generate molecular graphs with near-perfect validity, surpassing 1D baselines on MOSES.

Hojung Jung, Rodrigo Hormazabal, Jaehyeong Jo, Youngrok Park and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5
74%Highly rated
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NeurIPS 2026Université Paris CitéUniversité de ParisUniversity Paris-SaclayU CopenhagenGraph generation

A Topological Sorting Criterion for Random Causal Directed Acyclic Graphs

In randomly ordered DAGs, relative counts increase monotonically along causal order, enabling recovery via sorting and yielding singular equivalence classes.

Alexander Reisach, Antoine Chambaz, Gilles Blanchard, Sebastian Weichwald

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

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AI panel: 9 of 20 reviewers recommend it
lenient 3/5
medium 5/10
strict 1/5
86%Must read
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NeurIPS 2026U Toronto; McGillDepartment of Computer Science, McGill University, University ofMcGill University, McGillU TorontoGraph generation

Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generative Modeling

A neuro-symbolic framework pairs neural graph proposals with symbolic SMT solvers for hard-constraint satisfaction, achieving over 95% in-distribution and 64, 86% zero-shot rule compliance on the MolSAT benchmark.

Chuqin Geng, Li Zhang, Mark Zhang, Zhaoyue(Rebecca) Wang and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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14/20 AI panelreviewers recommend it

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AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 2/5