Graph Generation

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6 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Latest papers 32

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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
Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design
Oct 6, 2026Marcus Vukojevic, Erik Nielsen, Veronica Lachi +2Constrained Generative Modeling Graph Generation
University of Trento, Italy · UiT The Arctic University of Norway, Tromsø, Norway
Constrained Goal-directed Planar Graph Generation with Grammar-based Reinforcement Learning
Oct 5, 2026Nicolas Hochuli, Lorenzo Miele, Kristina Shea +1Constrained Generative Modeling Graph Generation
Engineering Design and Computing Laboratory ETH Zürich
Graph Data Augmentation via Contrastive Generator Inversion ( $\texttt{DCBA}$ )
Oct 5, 2026Mateusz Stolarski, Michał Czuba, Łukasz Kraiński +4Synthetic Data Augmentation Data Augmentation
Wrocław University of Science and Technology · University of Technology Sydney · SGH Warsaw School of Economics +1
Efficient Graph Generation via Direct Prediction and Flow Matching
Oct 4, 2026Susie LuFlow Matching Graph Generation
MIT
Stable Transformers for Graph Generation
Sep 30, 2026Luca Miglior, Alessio Gravina, Davide BacciuRepresentation Collapse Graph Generation
Department of Computer Science, University of Pisa, Italy
Autoregressive Frontier Expansion: Growing Trees with Graph Machine Learning
Sep 29, 2026Umer Gupta, Saku Peltonen, Martin RitzertGraph Generation Autoregressive Generation
Independent Researcher London, United Kingdom · ETH Zurich Zurich, Switzerland · Leipzig University Leipzig, Germany
Growth-Inspired Graph Generation and Inverse Design of Mechanical Lattices via Dot Matrices Database Augmentation and GCNN
Sep 24, 2026Weiyun Xu, Jiamu LiuGraph Generation Structural 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
ProtoGuide: Prototype-Driven Guidance for Class-Conditional Graph Generation
Sep 14, 2026Salvatore Romano, Marco Grassia, Pietro Liò +1Graph Generation Diffusion 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
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 Augmentation Graph 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
From topology learning to graph generation: A unifying perspective
Sep 2, 2026Xiaowen Dong, Hoi-To Wai, Siheng Chen +2Graph Structure Learning Graph Generation
University of Oxford · The Chinese University of Hong Kong · Shanghai Jiao Tong University +2
When Evidence Shapes Collaboration: Knowledge-Conditioned Topology Generation for Multi-Agent Systems
Aug 28, 2026Yangxiao Jiang, Jiarun Fan, Mingcong Xu +6Multi-Agent Systems Graph Generation
Huazhong University of Science and Technology
ReGraph: Learning to Generate Recipe Graphs from Food Images
Aug 7, 2026Guoshan Liu, Bin Zhu, Pengkun Jiao +3Graph Generation Graph Representation Learning
Institute of Trustworthy Embodied AI, Fudan University, China and Shanghai Key Laboratory of Multimodal Embodied AI, China · Singapore Management University, Singapore
When Can You Correct Distribution Drift in Temporal Graph Generation? A Sharpening--Drift Tension and an Impossibility for Observation-Based Correction
Jul 27, 2026Tianpeng Li, Xuan Guo, Wenjun Wang +2Flow Matching Distribution Shift
College of New Media and Communication, Tianjin University · School of Artificial Intelligence, Tianjin University · School of Cyberspace Security,Hangzhou Dianzi University
Learning and Structurally Validating Simulation Scenario Continuations in Dynamic Graph Systems
Jul 23, 2026Michael Romei de Socio, Gian Luca Pozzato, Alessio MerloConstrained Generative Modeling Graph Generation
CASD – School of Advanced Defense Studies, 00165 Rome, Italy · Department of Computer Science, University of Turin, 10149 Turin, Italy
Parallel Noising in Neural Markov Logic Networks
Jul 21, 2026Peter Jung, Giuseppe Marra, Ondrej KuzelkaMarkov Chain Monte Carlo Graph Generation
Czech Technical University, Prague, Czech Republic · Department of Computer Science, KU Leuven, Leuven, Belgium
The GEST-Engine: From Event Graphs to Synthetic Video. A Full Technical Report
Jul 14, 2026Nicolae Cudlenco, Mihai Masala, Marius LeordeanuGraph Generation Video Generation
Institute of Mathematics of the Romanian Academy, Bucharest, Romania · Büchi Labortechnik AG, Flawil, Switzerland · National University of Science and Technology Politehnica Bucharest, Romania
DiPhon: Diffusion on Graphons for Scalable Graph Generation
Jul 8, 2026Sergio Rozada, Yiming Qin, Manuel Madeira +2Graph Generation Graph Diffusion
King Juan Carlos University · EPFL, Lausanne, Switzerland · University of Pennsylvania
An LLM-Based Framework for Intent-Driven Network Topology Design
Jul 1, 2026Kholoud El-Habbouli, Fen Zhou, Stephane HuetLLM Evaluation Graph Generation
CERI-LIA, University of Avignon, France
Generative Lane Topology Reasoning via Autoregressive Model with Geometry Prior
Jun 30, 2026Jiahui Fu, Zehao Huang, Han Li +2Graph Generation Autonomous Driving Perception
Institute of Artificial Intelligence, Beihang University
An Information Theoretic Framework for Graph Novelty Generation via Latent Mixture Modeling
Jun 18, 2026Itsuki Nakagawa, Kenji YamanishiGraph Generation
Graduate School of Information Science and Technology, The University of Tokyo
FLAGG: Flexible Autoregressive Graph Generation
Jun 3, 2026Samuel Cognolato, Alessandro Sperduti, Luciano SerafiniGraph Generation Autoregressive Generation
Department of Mathematics, University of Padova, Padova, VE, 35121 Italy · Fondazione Bruno Kessler (FBK), Trento, TN, 38123 Italy · Department of Information Engineering and Computer Science, University of Trento, Trento, TN, 38123 Italy
Scaling Novel Graph Generation via Lightweight Structure-Guided Autoregressive Models
Jun 2, 2026Alessio Barboni, Massimiliano Lupo Pasini, Bishal Lakha +1Graph Generation Generative Modeling
Boise State University Boise, Idaho, USA · Oak Ridge National Laboratory Oak Ridge, Tennessee, USA
Efficient Synthetic Network Generation via Latent Embedding Reconstruction
May 31, 2026Feifan Jiang, Yinan Bu, Shihao Wu +2Graph Generation Synthetic Data Generation
Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA.
Evolutionary Refinement of Generative Graph Topologies: A Hybrid WGAN-GA Approach
May 27, 2026James Sargant, Seyedeh Ava Razi Razavi, Renata Dividino +1Graph Generation Generative Adversarial Networks
Computer Science Brock University, Canada
Reinforcement Learning for Graph Generation under a Hard Assortativity Constraint
May 22, 2026Hoyun Choi, Junghyo Jo, Deok-Sun LeeGraph Rewiring Graph Generation
School of Computational Sciences, Korea Institute for Advanced Study, 85, Hoegi-ro, 02455, Seoul, Korea. · Department of Physics Education, Seoul National University, 1, Gwanak-ro, 08826, Seoul, Korea. · Center for Theoretical Physics and Artificial Intelligence Institute, Seoul National University, 1, Gwanak-ro, 08826, Seoul, Korea. +1
When Graph Language Models Go Beyond Memorization
May 7, 2026Masatsugu Yamada, Mahito SugiyamaGraph Generation Memorization in Language Models
National Institute of Informatics Tokyo, Japan · National Institute of Informatics, Tokyo, Japan · SOKENDAI, Kanagawa, Japan
Fine-Grained Graph Generation through Latent Mixture Scheduling
May 4, 2026Nidhi Vakil, Hadi AmiriGraph Generation Generative Modeling
Department of Computer Science University of Massachusetts Lowell
Information-geometric adaptive sampling for graph diffusion
Apr 30, 2026Yuhui Lu, Wenjing Liu, Kun ZhanInformation Geometry Graph Generation
School of Information Science and Engineering, Lanzhou University, Gansu, China.
VesselTok: Tokenizing Vessel-like 3D Biomedical Graph Representations for Reconstruction and Generation
Mar 19, 2026Chinmay Prabhakar, Bastian Wittmann, Tamaz Amiranashvili +6Graph Generation Graph Representation Learning
University of Zurich, Switzerland · ETH AI Center, Zurich, Switzerland · Technical University of Munich, Germany +1

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Scalable Hierarchical Graph Generation via Soft Community Structure

Ahmet Tüzen, Helge Langseth, Kjetil Nørvåg

Department of Computer Science Norwegian University of Science and Technology

Graph Generation

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Generating large attributed graphs requires reproducing the topology, generating attributes jointly with the structure, and remaining scalable. Many real-world graphs exist as a single large graph, so a generative model has to generalize from the one graph it is fit on, without independent samples. We present Schema, which recursively decomposes a reference graph into a hierarchy of soft communities, assigning each node a membership distribution. Generation is then split into three stages, each trained independently: (1) synthesizing node attributes conditioned on soft memberships, (2) generating intra-community edges from local structural context, and (3) modeling inter-community connections over bridge nodes whose membership mass is distributed across several communities. No stage forms the full adjacency matrix, and each stage operates on a subgraph bounded by the community size. We also introduce an evaluation protocol that covers structural fidelity, memorization, downstream utility, and scalability. On four real-world attributed graphs, Schema recovers the balance between local and long-range structure more closely than any other model that generates attributes, while reproducing only a small fraction of the reference edges. It retains the downstream accuracy of the reference graph without raising it artificially above that level. Baselines that match its structural fidelity memorize the reference, while those with higher downstream accuracy either exceed the reference accuracy or fail to complete on the larger graphs. We measure scalability on six additional graphs with up to 10 million nodes.

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Latest papers 32