Graph Diffusion

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6 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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Latest papers 52

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Whole-Body Inverse Kinematics with Graph Diffusion
May 23, 2026Helong Huang, Kai Tan, Feng Wen +2Graph Neural Networks Inverse Kinematics
Large Model Algorithm Lab, Huawei
Graph Navier Stokes Networks
May 20, 2026Zexing Zhao, Guangsi Shi, Yu Gong +4Graph Neural Networks Graph Diffusion
Northwest A&F University Yangling, Shaanxi, China · Corporate Research Center, Midea Group Shanghai, China · Bosch Shanghai, China
Diffusion Graph Posterior Sampling for Nonlinear Inverse Problems with Application to Electrical Impedance Tomography
May 19, 2026Giovanni S. Alberti, Damiana Lazzaro, Serena Morigi +2Image Reconstruction Diffusion-Based Inverse Problems
MaLGa Center, Department of Mathematics, University of Genova, Italy · Department of Mathematics, University of Bologna, Bologna, Italy · Department of Mathematics, Harbin Institute of Technology, China
Deep Neural Sheaf Diffusion
May 18, 2026Rémi Bourgerie, Šarūnas Girdzijauskas, Viktoria FodorGraph Neural Networks Sheaf Neural Networks
School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Conditional Entropy of Heat Diffusion on Temporal Networks
May 15, 2026Samuel Koovely, Alexandre BovetChange-Point Detection Graph Diffusion
Department of Mathematical Modeling and Machine Learning University of Zurich Zurich, Switzerland
Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion
May 11, 2026Arif Dönmez, Axel Mosig, Ellen Fritsche +1Graph Neural Networks Sheaf Neural Networks
IUF – Leibniz Research Institute for Environmental Medicine, Düsseldorf, Germany · DNTOX GmbH, Düsseldorf, Germany · Bioinformatics Group, Ruhr University Bochum, Bochum, Germany +1
Marrying Generative Model of Healthcare Events with Digital Twin of Social Determinants of Health for Disease Reasoning
May 10, 2026Ziquan Wei, Tingting Dan, Guorong WuHealthcare Digital Twins
Department of Computer Science 2Department of Psychiatry University of North Carolina at Chapel Hill
Dependency-Aware Discrete Diffusion for Scene Graph Generation
May 9, 2026Rajalaxmi Rajagopalan, Romit Roy ChoudhuryScene Graph Generation Diffusion Models
University of Illinois, Urbana-Champaign
Unlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion
May 7, 2026Xujun Che, Xiuxia Du, Depeng XuGraph Neural Networks Molecular Graph Generation
Department of Software and Information Systems,2026 University of North Carolina at Charlotte, Charlotte, NC 28223 · Center for Environmental Monitoring and Informatics Technologies for Public Health University of North Carolina at Charlotte, Charlotte, NC 28223 · Department of Bioinformatics and Genomics,May University of North Carolina at Charlotte, Charlotte, NC 28223
GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model
May 7, 2026Shaozhen Ma, Wei Huang, Hanchen Wang +2Conditional Generative Modeling Consistency Models
University of New South Wales · University of Technology Sydney
GeoTopoDiff: Learning Geometry--Topology Graph Priors through Boundary-Constrained Mixed Diffusion for Sparse-Slice 3D Porous Reconstruction
May 5, 2026Yue Shi, Peng Wang, Mingzhe Yu +53D Reconstruction Sparse-View CT Reconstruction
Manchester Metropolitan University, Manchester M1 5GD, U.K. · University of Surrey, Surrey, GU2 7XH, U.K. · Johnson Matthey Billingham, TS23 1LB, U.K. +1
GD4: Graph-based Discrete Denoising Diffusion for MIMO Detection
May 1, 2026Qincheng Lu, Sitao Luan, Xiao-Wen ChangDiffusion Model Inference Acceleration Diffusion Models
School of Computer Science McGill University Montreal, Canada · University of Montreal Mila - Quebec AI Institute Montreal, Canada
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.
Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guarantees
Apr 21, 2026Zhuoping Zhou, Davoud Ataee Tarzanagh, Sima Didari +7Multi-Hop QA Graph Retrieval-Augmented Generation
Samsung SDS Research America, Mountain View, CA, USA
One Pass for All: A Discrete Diffusion Model for Knowledge Graph Triple Set Prediction
Apr 20, 2026Jihong Guan, Jiaqi Wang, Wengen Li +3Link Prediction KG Completion
School of Computer Science and Technology, Tongji University, Shanghai, China · School of Computer Science, Fudan University, Shanghai, China
Enhancing Discrete Particle Swarm Optimization for Hypergraph-Modeled Influence Maximization
Apr 17, 2026Qianshi Wang, Xilong Qu, Wenbin Pei +2Metaheuristic Optimization Particle Swarm Optimization
International School of Information Science & Engineering, Dalian University of Technology, Dalian 116024, China · School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China; Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education, Dalian 116024, China · School of Artificial Intelligence, Shanxi University, Taiyuan 237016, China +1
Beyond the Laplacian: Doubly Stochastic Matrices for Graph Neural Networks
Apr 16, 2026Zhaobo Hu, Vincent Gauthier, Mehdi NaimaGraph Neural Networks Graph Diffusion
SAMOVAR, Télécom SudParis · Institut Polytechnique de Paris · CNRS – LIP6 +1
Out-of-Distribution Detection in Molecular Complexes via Diffusion Models for Irregular Graphs
Dec 20, 2025David Graber, Victor Armegioiu, Rebecca Buller +1Diffusion-Based OOD Detection OOD Detection
Seminar for Applied Mathematics, Department of Mathematics and ETH AI Center, ETH Zurich, 8092 Zurich, Switzerland · Institute for Computational Life Sciences, Zurich University of Applied Sciences, 8820 Waedenswil, Switzerland · Department of Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, 3012 Bern, Switzerland
Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size
Oct 6, 2025Farid Bozorgnia, Vyacheslav Kungurtsev, Shirali Kadyrov +1Graph-Based Semi-Supervised Learning Graph Diffusion
Double-Diffusion: Balancing Speed, Accuracy, and Uncertainty in Probabilistic Forecasting for Urban Sensor Networks
Jun 29, 2025Hanlin Dong, Arian Prabowo, Hao Xue +4Spatiotemporal Forecasting Diffusion Model Inference Acceleration
University of New South Wales Sydney, Australia · University of Maryland College Park, United States · The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China
Backdoor Attacks on Discrete Graph Diffusion Models
Mar 8, 2025Jiawen Wang, Samin Bin Karim, Yuan Hong +1Graph Generation Backdoor Attacks
Illinois Institute of Technology · University of Connecticut
Parametrized Power-Iteration Clustering for Directed Graphs
Oct 1, 2022Gwendal Debaussart-Joniec, Harry Sevi, Matthieu Jonckheere +1Graph Clustering Graph Embedding
Universit´e Paris-Saclay, ENS Paris-Saclay, Centre Borelli, CNRS, France · CNRS, LAAS, France

May 23, 2026, cs.ROJ/K move · Enter open · S save

Whole-Body Inverse Kinematics with Graph Diffusion

Helong Huang, Kai Tan, Feng Wen, Guowei Huang+1

Large Model Algorithm Lab, Huawei

Graph Neural Networks Inverse Kinematics Graph Diffusion

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Inverse kinematics (IK) is a fundamental problem in robotics, requiring the generation of joint configurations that satisfy target end-effector poses. Existing approaches often struggle to generalize across diverse robot morphologies and to effectively model the multi-modal nature of IK, particularly in articulated systems with multiple kinematic branches. In this work, we propose GraphDiff-IK, a structure-aware graph diffusion framework for inverse kinematics. Specifically, we represent the robot as a kinematic graph constructed from the robot URDF, where nodes correspond to actuated joints and edges encode kinematic dependencies. Building upon this representation, we formulate IK as a conditional graph diffusion process that directly generates joint configurations on the robot graph. To better capture structural dependencies in articulated systems, we further introduce a structure-aware graph reasoning framework with hierarchical stage-wise message passing and torso-aware conditioning for multi-branch robots. In addition, we incorporate noisy forward kinematics feedback and task-space supervision to improve geometric consistency during denoising. The proposed framework provides a unified formulation that naturally supports single-arm robots, dual-arm systems, and articulated robots with torso or waist structures. Extensive experiments on diverse robotic platforms demonstrate that the proposed method achieves accurate and stable IK performance while preserving the ability to generate multiple feasible solutions for redundant robotic systems.

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