Graph Generation

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Overview

Graph generation studies how to create networks whose nodes, edges, and attributes reproduce useful patterns or meet specified constraints, supporting tasks such as molecule design, network simulation, and structured data synthesis. Common approaches include autoregressive models that add nodes or edges sequentially, diffusion and flow-matching models that denoise or transform graphs, and latent-variable, graphon, reinforcement-learning, and evolutionary methods. Models are evaluated on synthetic networks, molecular benchmarks, and application-specific graphs for structural fidelity, validity, diversity, novelty, and computational cost. A central challenge is scaling to large or variable-size graphs while balancing distributional accuracy and novelty with hard constraints, controllability, and protection against memorization or bias.

Latest papers

CardsList

69 papers18 papers in the last four weeks, up 125% on the four weeks before. 0.1% of all new papers.

  1. Beyond Reachability: When Edit Order Preserves Couplings in Graph Flow Matching

    Oct 28, 2026Zilin Li, Zixing SongGraph GenerationFlow Matching

    Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University

  2. Learning a Prior-Data Fitted Network for Graph Foundation Models via Structural Causal Model Priors

    Oct 28, 2026Ziwei Zhang, Tianyin Liao, Chunyu Hu +7Graph Foundation ModelsPrior-Data Fitted Networks

    Beihang University Haidian District, Beijing, China · Nankai University Binhai New Area, Tianjin, China · Tsinghua University Haidian District, Beijing, China

  3. H-GLow: Accelerating Graph Generation via Hazard-Guided Discrete Flow Matching

    Oct 24, 2026Sirine Ayadi, Stephan GünnemannGraph GenerationDiscrete Flow Matching

    School of Computation, Information and Technology, Technical University of Munich · Munich Data Science Institute & Munich Center for Machine Learning

  4. WL-SeqVAE: Sequence-Based Graph Generation Using Variational Autoencoders and Weisfeiler-Leman Colors

    Oct 23, 2026Iakovos Evdaimon, Giannis Nikolentzos, Achilleas Tsimichodimos +2Graph GenerationMolecular Graph Generation

    ´Ecole Polytechnique, IP Paris, France · University of Peloponnese, Greece · National Technical University of Athens, Greece +1

  5. Trajectory Matching: Discrete Flow Matching with Structured Actions

    Oct 23, 2026Niklas Kemper, Marten Lienen, David Lüdke +1Discrete Flow MatchingStructured Output Generation

    School of Computation, Information and Technology & Munich Data Science Institute, Technical University of Munich

  6. xVGAE: A Hierarchical Variational Graph Autoencoder for Exchangeable Graphs

    Oct 22, 2026Daniele Micheletti, Federica Zoe Ricci, Erik B. SudderthGraph GenerationVariational Autoencoders

    University of California, Irvine · Swarthmore College

  7. Efficient Edge-aware Attention Network for Graph Generation

    Oct 20, 2026Vincent Jung, Alba Carballo-Castro, Yiming Qin +2Graph GenerationGraph Transformer

    LTS4, EPFL, Lausanne, Switzerland · Università della Svizzera italiana, Lugano, Switzerland

  8. Beyond Trajectory Statistics: Community-Consistent Human Mobility Generation via Evolving User-Location Graphs

    Oct 20, 2026Bo Liu, Tong Li, Jiapeng Zhang +3Trajectory GenerationGraph Generation

    National Supercomputing Center in Changsha Hunan University Changsha, China

  9. Logic-Guided Graph Denoising Diffusion Models

    Oct 16, 2026Samuel Cognolato, Luciano Serafini, Alessandro Sperduti +1Graph GenerationConstrained Generative Modeling

    Department of Mathematics “Tullio Levi-Civita”, University of Padova, Padova, Italy · Fondazione Bruno Kessler (FBK), Trento, Italy · Human Inspired Technology Research Centre (HIT), University of Padova, Padova, Italy +1

  10. SNACK: A Sequential Notation Framework for Probabilistic Graph Generation

    Oct 14, 2026Hohyun Kim, Hyesung Kim, Min-hwan Oh +1Graph GenerationConstrained Generative Modeling

    Seoul National University

  11. When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors

    Oct 9, 2026Adarsh Jamadandi, Nicolas Keriven, Aline RoumyGraph GenerationGraph Diffusion

    CNRS, IRISA, Rennes, France1 · INRIA, Rennes, France2

  12. Scalable Hierarchical Graph Generation via Soft Community Structure

    Oct 8, 2026Ahmet Tüzen, Helge Langseth, Kjetil NørvågGraph Generation

    Department of Computer Science Norwegian University of Science and Technology

  13. Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design

    Oct 6, 2026Marcus Vukojevic, Erik Nielsen, Veronica Lachi +2Constrained Generative ModelingGraph Generation

    University of Trento, Italy · UiT The Arctic University of Norway, Tromsø, Norway

  14. Constrained Goal-directed Planar Graph Generation with Grammar-based Reinforcement Learning

    Oct 5, 2026Nicolas Hochuli, Lorenzo Miele, Kristina Shea +1Constrained Generative ModelingGraph Generation

    Engineering Design and Computing Laboratory ETH Zürich

  15. Graph Data Augmentation via Contrastive Generator Inversion (DCBA\texttt{DCBA})

    Oct 5, 2026Mateusz Stolarski, Michał Czuba, Łukasz Kraiński +4Synthetic Data AugmentationData Augmentation

    Wrocław University of Science and Technology · University of Technology Sydney · SGH Warsaw School of Economics +1

  16. Efficient Graph Generation via Direct Prediction and Flow Matching

    Oct 4, 2026Susie LuFlow MatchingGraph Generation

    MIT

  17. Discrete Diffusion for Large Graph Generation via Structural Candidate Restriction

    Oct 2, 2026Yassin Mohamadi, Zeno Geradts, Marcel WorringGraph GenerationDiffusion Models

  18. A Generative Model of Complex Networks Using Graphons and Neural Inverse Operators

    Oct 1, 2026Wooseong Choi, Italo'Ivo Lima Dias Pinto, Chen Sun +3Graphon TheoryGraph Generation

    Department of Biomedical Engineering University of Southern California Los Angeles, CA 90089 · Department of Electrical Engineering University of Southern California Los Angeles, CA 90089 · AWS AI Santa Clara, CA 95050

  19. Stable Transformers for Graph Generation

    Sep 30, 2026Luca Miglior, Alessio Gravina, Davide BacciuRepresentation CollapseGraph Generation

    Department of Computer Science, University of Pisa, Italy

  20. Autoregressive Frontier Expansion: Growing Trees with Graph Machine Learning

    Sep 29, 2026Umer Gupta, Saku Peltonen, Martin RitzertGraph GenerationAutoregressive Generation

    Independent Researcher London, United Kingdom · ETH Zurich Zurich, Switzerland · Leipzig University Leipzig, Germany

  21. Growth-Inspired Graph Generation and Inverse Design of Mechanical Lattices via Dot Matrices Database Augmentation and GCNN

    Sep 24, 2026Weiyun Xu, Jiamu LiuGraph GenerationStructural Mechanics

    Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA · State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

  22. ProtoGuide: Prototype-Driven Guidance for Class-Conditional Graph Generation

    Sep 14, 2026Salvatore Romano, Marco Grassia, Pietro Liò +1Graph GenerationDiffusion Guidance

    Department of Electrical, Electronic and Computer Engineering, University of Catania, Italy · Department of Computer Science and Technology, University of Cambridge, UK · University Campus Bio-Medico of Rome, Italy

  23. SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking

    Sep 9, 2026Guilherme Henrique Messias, Mariana Caravanti de Souza, Sylvia Iasulaitis +1Synthetic Data AugmentationGraph Generation

    Federal University of São Carlos, São Carlos, São Paulo, Brazil · Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul, Brazil

  24. Structural Entropy-Driven Graph Diffusion Generation for One-Shot Federated Graph Learning

    Sep 6, 2026Shutong Zheng, Lele Fu, Sheng Huang +2Federated Graph LearningNon-IID Federated Learning

    Sun Yat-sen University Guangzhou, China · Nanyang Technological University Singapore

  25. Connectome-to-Function: Conditional Generative Latent Representations for Reservoir Computing

    Sep 5, 2026Zhuolin Yu, Xingyu Liu, Yuanhao Jia +4Graph Representation LearningGraph Generation

    National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China · School of Electronics Engineering and Computer Science, Peking University, Beijing, China · School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China +2

  26. Inference-Time Graph Engineering for Multi-Agent LLM Workflows

    Sep 4, 2026Katherine Tieu, Dongqi Fu, Yinglong Xia +3Multi-Agent LLM SystemsMulti-Agent Orchestration

    Meta · University of Illinois Urbana-Champaign · Work done at Meta

  27. Embedded Graph Flows for Categorical Graph Generation

    Sep 4, 2026Ethan Ma, Zihan Wang, Chris Siu Yeung Chow +5Graph GenerationMolecular Graph Generation

    School of Electrical Engineering and Computer Science, The University of Queensland, Australia · Institute for Molecular Bioscience, The University of Queensland, Australia · Qunar.com, China +2

  28. From topology learning to graph generation: A unifying perspective

    Sep 2, 2026Xiaowen Dong, Hoi-To Wai, Siheng Chen +2Graph Structure LearningGraph Generation

    University of Oxford · The Chinese University of Hong Kong · Shanghai Jiao Tong University +2

  29. When Evidence Shapes Collaboration: Knowledge-Conditioned Topology Generation for Multi-Agent Systems

    Aug 28, 2026Yangxiao Jiang, Jiarun Fan, Mingcong Xu +6Multi-Agent SystemsGraph Generation

    Huazhong University of Science and Technology

  30. Diffusion Transformers for Roof Graph Synthesis and Reconstruction

    Aug 26, 2026Daniel Panangian, Ksenia BittnerGraph GenerationConditional Generative Modeling

    The Remote Sensing Technology Institute German Aerospace Center (DLR), Wessling, Germany