Latest Graph Neural Networks Research Papers

The newest Graph Neural Networks papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Graph Neural Networks so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Graph Transformer Transferability for Flood Scusceptibility mapping in contrasting regions
  Sreenath Vemula, Filippo Gatti, Pierre Jehel · HAL (Le Centre pour la Comm... · Sep 7, 2026

  International audience...

• RAGDNet: A Region-Adjacency Graph for Semantic Segmentation of Mechanical Drawings Using Graph Convolutional Networks
  Alexandre Monnier Weil, Nicolas Hili, Yves Ledru · Zenodo (CERN European Organ... · Aug 1, 2026
• RAGDNet: A Region-Adjacency Graph for Semantic Segmentation of Mechanical Drawings Using Graph Convolutional Networks
  Alexandre Monnier Weil, Nicolas Hili, Yves Ledru · Zenodo (CERN European Organ... · Aug 1, 2026
• Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling
  Aaron Feller, Kris Deibler, Maxim Secor · arXiv · Jul 23, 2026

  Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembles in solution. We introduce EnsembleEGNN, a molecular ensemble foundation model that en…

• Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields
  Tianyu Li, Zhiwei Cao, Qingang Zhang, Ruihang Wang et al. · arXiv · Jul 22, 2026

  Neural surrogates are widely used in scientific machine learning for fast prediction of three-dimensional (3D) thermo-fluid fields. However, generating training data using conventional numerical solvers often incurs substantial computationa…

• GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models
  Alessandro Scalese, Santhanakrishnan Narayanan, Constantinos Antoniou · arXiv · Jul 21, 2026

  The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained…

• A Knowledge-Guided Factor Graph Neural Network for Event Detection in Simulated Non-Cooperative Space Targets
  Chengeng Gong, Na Li, Zhao Huijie, Jingyi Yan et al. · Remote Sensing · Jul 21, 2026

  Online abnormal event detection of non-cooperative space targets based on space-based optical observations is an urgent requirement for ensuring space security, particularly for sudden state changes such as attitude and orbit maneuver event…

• Adaptive temporal graph neural networks for detecting coordinated multi-stage cyber-attacks in enterprise systems
  Teguh Nurhadi Suharsono, Tri Basuki Kurniawan, Deshinta Arrova Dewi, Hafiz Muhammad Kurniawan · Discover Computing · Jul 21, 2026

  This paper presents the problem of detecting complex, coordinated cyber-attacks at multiple intervals across enterprise networks. Traditional intrusion detection systems lack the ability to identify the ‘when’ and ‘where’ of attacks, result…

• Energy-aware multi-agent sand-table formation control via cross-layer SAC–GNN and federated learning
  Ruting Li, Wenni Xiao, Yan Sun, Wei Jiang · Discover Artificial Intelli... · Jul 20, 2026

  In scaled-down sand table multi-agent formation experiments, existing control strategies often focus on path accuracy or formation stability, neglecting energy consumption constraints. This leads to a lack of energy awareness and inefficien…

• A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations
  Sangmin Oh, J.H. You, Jaesun Kim, Jiho Lee et al. · Journal of Chemical Informa... · Jul 20, 2026

  We introduce a lightweight universal machine-learning interatomic potential (uMLIP), SevenNet-Nano, based on the graph neural network architecture SevenNet and enabled by a knowledge-distillation framework. The model inherits the broad gene…

• Role of Artificial Intelligence in Drug Discovery and Repurposing: A Comprehensive Review
  Dwivedi S, Rajoriya H, Sen K, Subhash et al. · Zenodo (CERN European Organ... · Jul 19, 2026

  ABSTRACT Drug discovery is one of the most resource-intensive endeavours in modern science, requiring over 12 years and USD 2.6 billion on average to bring a single drug to market, with a clinical failure rate exceeding 90%. Artificial Inte…

• DLAGNN: A Dual-Layer Attention Graph Neural Network Framework with Dynamic Feature Fusion and Multi-Task Learning for Customer Relationship Management in E-Commerce
  Junyi Yang · Journal of Engineering Proj... · Jul 19, 2026

  The customer-centric transformation of e-commerce makes it difficult for traditional customer relationship management to meet the needs of enterprises to accurately tap into customer value. Therefore, the research aims to build an intellige…

• Graph Neural Networks for Financial Fraud and Anomaly Detection
  Raji N · Zenodo (CERN European Organ... · Jul 18, 2026

  Financial fraud now spreads through coordinated accounts whose risk is visible mainly in how they connect rather than in any single record. Tree based classifiers that score transactions in isolation miss this relational signal. This paper …

• Graph Neural Networks for Financial Fraud and Anomaly Detection
  Raji N · Zenodo (CERN European Organ... · Jul 18, 2026

  Financial fraud now spreads through coordinated accounts whose risk is visible mainly in how they connect rather than in any single record. Tree based classifiers that score transactions in isolation miss this relational signal. This paper …

• Adaptive Inference Neuro-Fuzzy Driven Embedding Fusion for Improving Link Prediction Through Graph Neural Network
  Phu Pham · International Journal of Un... · Jul 18, 2026

  Link prediction has long been regarded as a fundamental problem in networked data analysis and mining, owing to its importance in a wide range of real-world applications, including social network analysis, recommendation systems, and biolog…

• Multipole-enhanced machine-learning dipole moment predictions in non-equilibrium polycyclic aromatic hydrocarbons
  X X Xu, Xinghong Mai, Zhao Wang · Physica Scripta · Jul 17, 2026

  Abstract The accurate prediction of dipole moments for non-equilibrium molecular structures remains a challenge. This study uses a graph neural network to assess the value of incorporating higher-order multipoles, specifically, atomic dipol…

• Transfer Learning in Graph Neural Networks with Real-World Offshore Wind Farm Data
  Jan Van Rompaey, Francisco de Nolasco Santos, Wout Weijtjens, Christof Devriendt · e-Journal of Nondestructive... · Jul 17, 2026

  The ever-growing need for renewable energy has driven the development of increasingly large offshore wind turbines. Alongside improved design codes and changing control strategies, this has led to fatigue becoming an operational concern. Fa…

• Uncertainty-aware multi-fault diagnosis in gas turbines via graph neural networks and weighted total least squares
  Dakuan Xin, Juxi Hu, Ke Zhao, Siyuan Liu et al. · Applied Energy · Jul 17, 2026
• Multi-domain reviews for aspect-based sentimental analysis using multilevel fast point graph transformer network
  Nikhil Narayanan, R. Kalaiselvi, J. E. Judith · Knowledge and Information S... · Jul 16, 2026
• Multimodal Dataset of 583 Traditional Villages for Spatial Morphology Analysis in Jiangxi Province, China
  Jiaxin Zhang · Zenodo (CERN European Organ... · Jul 16, 2026

  This dataset accompanies the study “Multi-Modal Feature Fusion for Spatial Morphology Analysis of Traditional Villages via Hierarchical Graph Neural Networks.” It contains multimodal data for 583 traditional villages in Jiangxi Province, Ch…

• DST-CAN: A dynamic spatiotemporal convolutional attention network for short-term wind power forecasting
  Peng Chen, Danhong Zhang, Yixin Su · International Journal of Gr... · Jul 15, 2026

  With the continuous increase in wind power penetration, accurate and reliable short-term wind power forecasting is pivotal for optimizing grid dispatch and ensuring system stability. However, the complex spatiotemporal coupling within wind …

• A Temporal Graph Neural Network with Gated Recurrent Unit for robust in-situ prediction of milling tool remaining useful life
  Dingli Guo, Honggen Zhou, Guochao Li, Li Sun · Journal of Manufacturing Pr... · Jul 15, 2026
• Hybrid Graph Neural And Machine Learning Architecture For Complex Network Intelligence
  Roshan Rukshana Sulaima Lebbe, Padmaja C · Zenodo (CERN European Organ... · Jul 14, 2026

  The growing sophistication in the structure and behavior of current networked systems requires sophisticated methods that can effectively uncover and understand the complicated patterns and relationships in graph-based data. In this paper, …

• A node-aware Graph Neural Network-based carbon intensity forecasting model for cross-border power grids
  Xiaoyang Zhang, Dan Wang · Computers in Industry · Jul 13, 2026
• Explainable Graph Neural Networks Towards Data-Driven Inverse Kinematics in Industrial Robot Motion Planning
  Ali Jlidi, Rabab Benotsmane, László Kovács · Electronics · Jul 13, 2026

  Inverse kinematics (IK) is fundamental to robot motion planning. Classical analytical solvers require complete Denavit–Hartenberg (DH) parameters that are often proprietary or degraded by mechanical wear, and numerical solvers based on damp…

• ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures
  Xinyu Wang, Debswapna Bhattacharya · Journal of Chemical Informa... · Jul 13, 2026

  Deep-learning-based protein structure prediction methods have led to a paradigm shift in computational structural biology, yet reliably assessing the quality of computationally predicted multimeric structures remains challenging. Recent met…

• An End-to-End Deep Learning Framework for Sugarcane Quality Assessment Based on Near-Infrared Spectroscopy and Explicit Feature Interaction-Aware Graph Neural Networks
  P Praveen Yadav, Dr. Raj Kumar, Shantanu Yadav, Tanya Rana et al. · Zenodo (CERN European Organ... · Jul 11, 2026

  Accurate, rapid, and non-destructive assessment of sugarcane quality is a critical requirement for optimizing harvesting schedules, refinery throughput, and fair payment systems in the global sugar industry. Conventional wet chemistry metho…

• A graph neural network-based framework for organizational knowledge flow optimization in digital enterprises
  Balaji Magar · Machine learning for comput... · Jul 10, 2026
• EdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy
  Wenxiu Ding, Muzhi Liu, Zheng Yan, Mingjun Wang et al. · arXiv · Jul 9, 2026

  Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information. To satisfy edge-lev…

• Trajectory prediction model of airport flight area based on transformer network
  Qijun Zhang, Liu Y, Silin Li, Wenliang Miao et al. · OpenAlex · Jul 9, 2026

  A trajectory prediction network model based on airport road operation rules is proposed to ensure the safety of autonomous vehicles driving in the airport flight area. Firstly, the scene structure features of the airport flight area are ext…