Graph Neural Networks

Also known as GNN

Follow this topic Ask about this topic

Latest papers 686

88 papers in the last four weeks, up 31% on the four weeks before. 0.5% of all new papers.

All topics

Learning to Rank the Initial Branching Order of SAT Solvers
Mar 7, 2026Arvid Eriksson, Gabriel Poesia, Roman Bresson +2Constraint Satisfaction Learning to Rank
KTH Royal Institute of Technology · Kempner Institute at Harvard University · Mohamed Bin Zayed University of Artificial Intelligence
Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines
Mar 7, 2026Yuhang Song, Naima Abrar Shami, Romaric Duvignau +1Graph Sparsification Graph Neural Networks
Boston University · Chalmers University of Technology
SG-DOR: Learning Scene Graphs with Direction-Conditioned Occlusion Reasoning for Pepper Plants
Mar 6, 2026Rohit Menon, Niklas Mueller-Goldingen, Sicong Pan +2Occlusion Reasoning 3D SGG
Humanoid Robots Lab, University of Bonn, Germany. · Lamarr Institute and the Center for Robotics, Bonn.
Polarized Direct Cross-Attention Message Passing in GNNs for Machinery Fault Diagnosis
Mar 6, 2026Zongyu Shi, Laibin Zhang, Maoyin ChenGraph Neural Networks Fault Diagnosis
Department of Automation, College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing 102299, China · Department of Safety Engineering, College of Safety and Ocean Engineering, China University of Petroleum (Beijing), Beijing 102299, China
Few-Shot Neural Differentiable Simulator: Real-to-Sim Rigid-Contact Modeling
Mar 6, 2026Zhenhao Huang, Siyuan Luo, Bingyang Zhou +3Differentiable Physics Robot Dynamics Learning
National University of Singapore, Singapore · Prana Lab, San Francisco, USA
On the Necessity of Learnable Sheaf Laplacians
Mar 5, 2026Ferran Hernandez Caralt, Mar Gonzàlez i Català, Adrián Bazaga +1Sheaf Neural Networks Graph Neural Networks
Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom · Microsoft
Recurrent Graph Neural Networks and Arithmetic Circuits
Mar 5, 2026Timon Barlag, Vivian Holzapfel, Laura Strieker +2Graph Neural Networks GNN Expressivity
Institut für Theoretische Informatik, Leibniz Universität Hannover, Germany · School of Computing Science, University of Glasgow, UK
Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks
Mar 5, 2026Yuxiang Zhang, Bin Ma, Enyan DaiBackdoor Attacks Graph Neural Networks
AI Thrust, INFO Hub, The Hong Kong University of Science and Technology (Guangzhou)
LUMINA: Laplacian-Unifying Mechanism for Interpretable Neurodevelopmental Analysis via Quad-Stream GCN
Mar 5, 2026Minkyung Cha, Jooyoung Bae, Jaewon Jung +3Graph Neural Networks Medical Diagnosis
Seoul National University, Seoul, Republic of Korea · NVIDIA AI Technology Center HK, Hong Kong
An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs
Mar 4, 2026Waleed Afandi, Hussein Abdallah, Ashraf Aboulnaga +1Efficient Neural Network Inference Graph Neural Networks
Concordia University · University of Texas at Arlington
Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks
Mar 4, 2026Matteo De Sanctis, Riccardo De Sanctis, Stefano Faralli +2GNN Explainability Counterfactual Learning
Sapienza University of Rome, Rome, Italy · ISTC-CNR & Sapienza University of Rome, Rome, Italy · Technical University of Munich, Munich, Germany
Graph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling
Mar 4, 2026Jiaqi Lv, Qingfeng Du, Yu Zhang +2Graph Neural Networks Graph Heterophily
School of Computer Science and Technology, Tongji University, Shanghai, China
GNN For Muon Particle Momentum estimation
Mar 3, 2026Vishak K Bhat, Eric A. F. Reinhardt, Sergei GleyzerHigh-Energy Physics Graph Neural Networks
IIT Dhanbad, India · Department of Physics and Astronomy, The University of Alabama, USA
Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
Mar 2, 2026Semih Cantürk, Thomas Sabourin, Frederik Wenkel +2Neural Combinatorial Optimization Transfer Learning
Dept. of Computer Science and Operations Research, Université de Montréal · Mila – Quebec AI Institute · Dept. of Mathematics & Statistics, Université de Montréal +2
Graph neural network force fields for adiabatic dynamics of lattice Hamiltonians
Mar 2, 2026Yunhao Fan, Gia-Wei ChernGraph Neural Networks Hamiltonian Dynamics
Department of Physics, University of Virginia, Charlottesville, VA 22904, USA
Revealing Combinatorial Reasoning of GNNs via Graph Concept Bottleneck Layer
Mar 2, 2026Yue Niu, Zhaokai Sun, Jiayi Yang +5GNN Explainability Graph Neural Networks
Tongji University, Shanghai, China · City University of Macau, Macau, China
Phishing the Phishers with SpecularNet: Hierarchical Graph Autoencoding for Reference-Free Web Phishing Detection
Mar 2, 2026Tailai Song, Pedro Casas, Michela MeoGraph Neural Networks
Politecnico di Torino Turin, Italy · AIT Austrian Institute of Technology Vienna, Austria
Invariant-Stratified Propagation for Expressive Graph Neural Networks
Mar 2, 2026Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe +1Graph Neural Networks GNN Expressivity
Department of Mechanical Engineering, University of Melbourne, Melbourne, Australia · School of Computing, Australian National University, Canberra, Australia · Data61, CSIRO, Canberra, Australia
Super-resolution of turbulent reacting flows on complex meshes using graph neural networks
Mar 1, 2026Priyabrat Dash, Konduri Aditya, Christos E. Frouzakis +1Computational Fluid Dynamics Graph Neural Networks
FLAME Laboratory, Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India · CAPS Laboratory, Department of Mechanical and Process Engineering, ETH Zürich, 8092 Zürich, Switzerland · Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
TAS-GNN: A Status-Aware Signed Graph Neural Network for Anomaly Detection in Bitcoin Trust Systems
Feb 28, 2026Chang Xue, Fang Liu, Jiaye Wang +2Anomaly Detection Graph Neural Networks
Yeshiva University · Yale University · University of California, San Diego +2
Normalisation and Initialisation Strategies for Graph Neural Networks in Blockchain Anomaly Detection
Feb 27, 2026Dang Sy Duy, Nguyen Duy Chien, Kapil Dev +1Graph Neural Networks Neural Network Initialization
RMIT University, Hanoi, Vietnam · Vietnam National University, Hanoi, Vietnam
Rudder: Steering Prefetching in Distributed GNN Training using LLM Agents
Feb 26, 2026Aishwarya Sarkar, Sayan Ghosh, Nathan Tallent +3Communication-Efficient Distributed Training Graph Neural Networks
Iowa State University, Ames, IA, USA · Pacific Northwest National Laboratory, Richland, WA, USA · Amazon GenAI, Cupertino, CA, USA +1
Cross-Representation Knowledge Transfer for Improved Sequential Recommendations
Feb 26, 2026Artur Gimranov, Viacheslav Yusupov, Elfat Sabitov +4Sequential Recommendation Graph Neural Networks
HSE University, Moscow, Russian Federation · SB AI Lab, Moscow, Russian Federation
Reconstruction of cosmic-ray direction and energy in radio arrays using deep ensemble graph neural networks
Feb 26, 2026Arsène Ferrière, Aurélien Benoit-Lévy, Olivier Martineau-Huynh +1Graph Neural Networks Direction-of-Arrival Estimation
Université Paris-Saclay, CEA, List, F-91120 Palaiseau, France · Sorbonne Université, CNRS, Laboratoire de Physique Nucléaire et des Hautes Énergies (LPNHE), 4 Pl. Jussieu, 75005 Paris, France · National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China +2
LineGraph2Road: Structural Graph Reasoning on Line Graphs for Road Network Extraction
Feb 26, 2026Zhengyang Wei, Renzhi Jing, Yiyi He +1Graph Neural Networks Link Prediction
Stanford University, Stanford CA 94305, USA · Georgia Institute of Technology, Atlanta GA 30332, USA
Protein Graph Neural Networks for Heterogeneous Cryo-EM Reconstruction
Feb 25, 2026Jonathan Krook, Axel Janson, Joakim Andén +2Cryo-Electron Microscopy Graph Neural Networks
KTH Royal Institute of Technology · Harvard University Department of Mathematics School of Engineering and Applied Sciences
RABot: Reinforcement-Guided Graph Augmentation for Imbalanced and Noisy Social Bot Detection
Feb 25, 2026Longlong Zhang, Xi Wang, Haotong Du +3Data Augmentation Class-Imbalanced Learning
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University · Shenzhen Research Institute, Northwestern Polytechnical University · School of Automation Science and Engineering, Xi’an Jiaotong University +1
NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training
Feb 25, 2026Zhongwei Xie, Jiaxin Bai, Shujie Liu +5Graph Reasoning Neuro-Symbolic Reasoning
The Hong Kong University of Science and Technology · Microsoft Research Asia
MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
Feb 24, 2026Jesse He, Helen Jenne, Max Vargas +4Neural Algorithmic Reasoning Mechanistic Interpretability
Pacific Northwest National Laboratory, Richland, WA · Halıcı˘oğlu Data Science Institute, University of California, San Diego, San Diego, CA · Department of Computer and Information Science, University of Pennsylvania, Pennsylvaina, PA +1
GrapHist: Graph Self-Supervised Learning for Histopathology
Feb 24, 2026Sevda Öğüt, Cédric Vincent-Cuaz, Natalia Dubljevic +4Computational Pathology Graph SSL
LTS4, EPFL, Lausanne, 1015, Vaud, Switzerland

Mar 7, 2026J/K move · Enter open · S save

Learning to Rank the Initial Branching Order of SAT Solvers

Arvid Eriksson, Gabriel Poesia, Roman Bresson, Karl Henrik Johansson+1

KTH Royal Institute of Technology · Kempner Institute at Harvard University · Mohamed Bin Zayed University of Artificial Intelligence

Constraint Satisfaction Learning to Rank Graph Neural Networks

Open paper AskPDF arXiv

Finding good branching orders is key to solving SAT problems efficiently, but finding such branching orders is a difficult problem. Using a learning based approach to predict a good branching order before solving, therefore, has potential. In this paper, we investigate predicting branching orders using graph neural networks as a preprocessing step to conflict-driven clause learning (CDCL) SAT solvers. We show that there are significant gains to be made in existing CDCL SAT solvers by providing a good initial branching. Further, we provide three labeling methods to find such initial branching orders in a tractable way. Finally, we train a graph neural network to predict these branching orders and show through our evaluations that a GNN-initialized ordering yields significant speedups on random 3-CNF and pseudo-industrial benchmarks, with generalization capabilities to instances much larger than the training set. However, we also find that the predictions fail at speeding up more difficult and industrial instances. We attribute this to the solver's dynamic heuristics, which rapidly overwrite the provided initialization, and to the complexity of these instances, making GNN prediction hard.