Graph Neural Networks

Also known as GNN

Graph Neural Networks (GNNs) study how to learn from data whose entities and relationships form a graph, supporting tasks such as node classification, link prediction, and graph or molecular property prediction. Common approaches include message-passing networks such as GCN, GraphSAGE, and GIN, attention-based Graph Transformers, and spectral, geometric, or higher-order methods that encode richer structure. They are used on benchmarks such as Cora and ogbn-arxiv, as well as in molecular modeling, recommender systems, scientific simulation, and network control. Key open challenges include handling heterophilic graphs, preserving useful information in deep networks without oversmoothing or oversquashing, and achieving reliable generalization, robustness, and efficiency on large or shifting graphs.

Latest papers

751 papers73 papers in the last four weeks, down 13% on the four weeks before. 0.5% of all new papers.

COMBA: Cross Batch Aggregation for Learning Large Graphs with Context Gating State Space Models
Feb 19, 2026Jiajun Shen, Yufei Jin, Yi He +1Graph Representation Learning Graph Neural Networks
Florida Atlantic University · William & Mary
A Benchmark Analysis of Graph and Non-Graph Methods for Caenorhabditis Elegans Neuron Classification
Feb 19, 2026Jingqi Lu, Keqi Han, Yun Wang +2Classification Node Classification
Department of Computer Science, Emory University, USA · Department of Biomedical Informatics, Emory University, USA · College of AI, Tsinghua University, China
Semi-Supervised Learning on Graphs using Graph Neural Networks
Feb 19, 2026Juntong Chen, Claire Donnat, Olga Klopp +1Graph Neural Networks Graph-Based Semi-Supervised Learning
School of Mathematical Sciences, Xiamen University · Department of Statistics, University of Chicago · ESSEC Business School +1
A Locality Radius Framework for Understanding Relational Inductive Bias in Database Learning
Feb 19, 2026Aadi Joshi, Kavya BhandRelational Deep Learning Graph Neural Networks
Dept. of Computer Engineering, Vishwakarma Institute of Technology, Pune, India.
AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation
Feb 19, 2026Rong Fu, Chunlei Meng, Shuo Yin +2Graph Neural Networks Graph Structure Learning
Independent Researcher
Hardware-accelerated graph neural networks: an alternative approach for neuromorphic event-based audio classification and keyword spotting on SoC FPGA
Feb 18, 2026Kamil Jeziorek, Piotr Wzorek, Krzysztof Blachut +6Audio Classification Keyword Spotting
The AGH University of Krakow, Poland · Graduate School of Science and Technology, Keio University, Japan · CEA-List, Université Grenoble Alpes, France
Graph neural network for colliding particles with an application to sea ice floe modeling
Feb 18, 2026Ruibiao ZhuGraph Neural Networks Physics-Based Simulation
College of Systems and Society, The Australian National University, ACT, 2600, Australia.
Investigating GNN Convergence on Large Randomly Generated Graphs with Realistic Node Feature Correlations
Feb 18, 2026Mohammed Zain Ali AhmedGraph Neural Networks GNN Expressivity
Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors
Feb 17, 2026Erkan Turan, Gaspard Abel, Maysam Behmanesh +2GNN Oversmoothing Graph Neural Networks
LIX, Ecole Polytechnique, IP Paris · Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, F-91190, Gif-sur-Yvette, France · Centre d’Analyse et de Mathématique Sociales, EHESS, CNRS, 75006 Paris, France..
On the Geometric Coherence of Global Aggregation in Federated Graph Neural Networks
Feb 17, 2026Chethana Prasad Kabgere, Shylaja SSFederated Graph Learning Federated Learning Aggregation
Department of Computer Science and Engineering, PES University, Bengaluru, Karnataka, 560085, India
Learning to Approximate Uniform Facility Location via Graph Neural Networks
Feb 13, 2026Chendi Qian, Christopher Morris, Stefanie Jegelka +1Graph Neural Networks Combinatorial Optimization
RWTH Aachen University, Germany · Technical University of Munich, Germany · Massachusetts Institute of Technology, USA +1
Smoothness Errors in Dynamics Models and How to Avoid Them
Feb 5, 2026Edward Berman, Luisa Li, Jung Yeon Park +1Graph Neural Networks Over-Smoothing in GNNs
Geometric Learning Lab, Northeastern University.
Breaking the Reasoning Horizon in Entity Alignment Foundation Models
Jan 29, 2026Yuanning Cui, Zequn Sun, Wei Hu +2Graph Neural Networks Graph Foundation Models
1Nanjing University of Information Science and Technology · 2State Key Laboratory for Novel Software Technology, Nanjing University · 3National Institute of Healthcare Data Science, Nanjing University, Nanjing +2
FloydNet: A Learning Paradigm for Global Relational Reasoning
Jan 27, 2026Jingcheng Yu, Mingliang Zeng, Qiwei YeGraph Neural Networks Relational Reasoning
Beijing Academy of Artificial Intelligence
Graph Neural Networks are Heuristics
Jan 19, 2026Yimeng Min, Carla P. GomesTraveling Salesperson Problem Graph Neural Networks
Department of Computer Science Cornell University Ithaca, NY, USA
A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention
Jan 8, 2026Peixin Huang, Yaoxin Wu, Yining Ma +3Graph Neural Networks Mixed-Integer Linear Programming
Shenzhen Research Institute, Shandong University, China
Through the PRISm: Importance-Aware Scene Graphs for Semantic Image Retrieval
Dec 20, 2025Dimitrios Georgoulopoulos, Nikolaos Chaidos, Angeliki Dimitriou +1Image Retrieval Graph-Based Retrieval
National Technical University of Athens Athens, Greece
FuseSampleAgg: One-Pass Neighborhood Estimation for Budgeted Knowledge-Graph Refresh and Validation
Nov 17, 2025Aleksandar Stanković, Haoran Du, Xinming WangGraph Neural Networks GPU Kernel Optimization
University of Novi Sad, Novi Sad, Serbia · Shanghai Key Lab of Intelligent Information Processing,2026 College of Computer Science and Artificial Intelligence, Fudan University, China · Institute of Automation, Chinese Academy of Sciences, China
Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing
Nov 14, 2025Brian Godwin Lim, Galvin Brice Lim, Renzo Roel Tan +2Graph Neural Networks Graph Representation Learning
Nara Institute of Science and Technology, Kyoto University, and Ateneo de Manila University · UNI-President Information Philippines Corporation · Nara Institute of Science and Technology and Ateneo de Manila University +2
GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearning
Nov 14, 2025Ying Song, Balaji PalanisamyGraph Neural Networks Graph Unlearning
University of Pittsburgh Pittsburgh, PA, USA
Robust GNN Watermarking via Implicit Perception of Topological Invariants
Oct 29, 2025Jipeng Li, Yanning ShenGNN Watermarking Robust Watermarking
Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA 95616, USA · Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA 92697, USA
PRGCN: A Graph Memory Network for Cross-Sequence Pattern Reuse in 3D Human Pose Estimation
Oct 22, 2025Zhuoyang Xie, Yibo Zhao, Hui Huang +2Memory-Augmented Neural Networks Graph Neural Networks
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, P.R China. · the Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, 300384, China. · School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, 325000, P.R China. +1
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Oct 13, 2025Jun-En Ding, Anna Zilverstand, Shihao Yang +2Alzheimer's Disease Electroencephalography
Department of Industrial and Systems Engineering, Rutgers University, Piscataway, NJ, USA · Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA · Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University, Taipei City, Taiwan
Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans
Oct 12, 2025Theo Di Piazza, Carole Lazarus, Olivier Nempont +1Graph Neural Networks Graph Representation Learning
INSA Lyon, University of Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Villeurbanne, France · Hospices Civil de Lyon, Lyon, France · Philips Clinical Informatics, Innovation Paris, France
CRPS-LAM: Probabilistic Regional Weather Forecasting with Continuous Ranked Probability Score
Oct 10, 2025Erik Larsson, Joel Oskarsson, Tomas Landelius +1Graph Neural Networks Ensemble Learning
Linköping University · ETH AI Center · SMHI & Linköping University
Fractional Heat Kernel for Semi-Supervised Graph Learning with Small Training Sample Size
Oct 6, 2025Farid Bozorgnia, Vyacheslav Kungurtsev, Shirali Kadyrov +1Over-Smoothing in GNNs Graph-Based Semi-Supervised Learning
Toward Uncertainty-Aware and Generalizable Neural Decoding for Quantum LDPC Codes
Oct 5, 2025Xiangjun Mi, Frank MuellerQuantum LDPC Codes Quantum Error Correction Decoding
North Carolina State University, Raleigh, NC 27695, USA
GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
Sep 28, 2025Fanlong Zeng, Wensheng Gan, Kangjie Chen +1Class-Imbalanced Learning Graph Neural Networks
School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China · School of Cybersecurity, Tianjin University, Tianjin 300354, China · University of Illinois Chicago, Chicago, USA
Self-Improving Neural Pruning: A Graph Neural Network Framework for Scalable Mixed Bundle Pricing
Sep 26, 2025Liangyu Ding, Chenghan Wu, Guokai Li +1Graph Neural Networks Combinatorial Optimization
School of Data Science, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
Tackling GNARLy Problems: Graph Neural Algorithmic Reasoning Reimagined through Reinforcement Learning
Sep 23, 2025Alex Schutz, Victor-Alexandru Darvariu, Efimia Panagiotaki +2Graph Neural Networks Reinforcement Learning
Oxford Robotics Institute, University of Oxford · Stateful Robotics

Feb 19, 2026J/K move · Enter open · S save

COMBA: Cross Batch Aggregation for Learning Large Graphs with Context Gating State Space Models

Jiajun Shen, Yufei Jin, Yi He, xingquan Zhu

Florida Atlantic University · William & Mary

Graph Representation Learning Graph Neural Networks State Space Models

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State space models (SSMs) have recently emerged for modeling long-range dependency in sequence data, with much simplified computational costs than modern alternatives, such as transformers. Advancing SMMs to graph structured data, especially for large graphs, is a significant challenge because SSMs are sequence models and the shear graph volumes make it very expensive to convert graphs as sequences for effective learning. In this paper, we propose COMBA to tackle large graph learning using state space models, with two key innovations: graph context gating and cross batch aggregation. Graph context refers to different hops of neighborhood for each node, and graph context gating allows COMBA to use such context to learn best control of neighbor aggregation. For each graph context, COMBA samples nodes as batches, and train a graph neural network (GNN), with information being aggregated cross batches, allowing COMBA to scale to large graphs. Our theoretical study asserts that cross-batch aggregation guarantees lower error than training GNN without aggregation. Experiments on benchmark networks demonstrate significant performance gains compared to baseline approaches. Code and benchmark datasets will be released for public access.