Deep learning · graph neural networks papers, 2023–2026

Key findings

• Deep learning · graph neural networks fell from 728 papers in 2023 to 326 in 2026, so interest is cooling.
• NeurIPS publishes the most Deep learning · graph neural networks work: 842 papers from 2023 to 2026, 51% of the four-venue total.
• The fastest-growing overlap is with Datasets and Benchmarks: papers tagged with both rose from 9 in 2023 to 20 in 2026.
• Among the 11 topics under Deep Learning, it ranks #5 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 0.4x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A New Perspective on Target-Conditioned Structural Dynamics for Link Prediction in Dynamic Graphs
• ICML 2026A Graph Foundation Model with Cross-Modal Alignment and Modality-Aware Expert Fusion for Multi-Modal Graphs
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
• NeurIPS 2025Depth-Width Tradeoffs for Transformers on Graph Tasks
• ICML 2025A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
• ICLR 2025Factor Graph-based Interpretable Neural Networks
• CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
• ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
• CVPR 2024Learning Structure-from-Motion with Graph Attention Networks
• NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
• ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
• ICLR 2023Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
• CVPR 2023An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity
• NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
• ICML 2026A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
• ICLR 2026GTool: Graph Enhanced Tool Planning with Large Language Model
• CVPR 2026GSNR Graph Smooth Null-Space Representation for Inverse Problems

All Deep learning · graph neural networks papers

1,648 papers, page 22 of 33

1051. ICML 2023Efficiently predicting high resolution mass spectra with graph neural networks
1052. ICML 2023Equivariance with Learned Canonicalization Functions
1053. ICML 2023Equivariant Polynomials for Graph Neural Networks
1054. ICML 2023Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
1055. ICML 2023Expectation-Complete Graph Representations with Homomorphisms
1056. ICML 2023Feature Expansion for Graph Neural Networks
1057. ICML 2023Featured Graph Coarsening with Similarity Guarantees
1058. ICML 2023Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
1059. ICML 2023From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
1060. ICML 2023Function-Space Regularization in Neural Networks: A Probabilistic Perspective
1061. ICML 2023GC-Flow: A Graph-Based Flow Network for Effective Clustering
1062. ICML 2023GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency
1063. ICML 2023Generalizing Neural Wave Functions
1064. ICML 2023Generated Graph Detection
1065. ICML 2023Generative Graph Dictionary Learning
1066. ICML 2023Geometric Clifford Algebra Networks
1067. ICML 2023GFlowOut: Dropout with Generative Flow Networks
1068. ICML 2023Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks
1069. ICML 2023Graph Contrastive Backdoor Attacks
1070. ICML 2023Graph Generative Model for Benchmarking Graph Neural Networks
1071. ICML 2023Graph Inductive Biases in Transformers without Message Passing
1072. ICML 2023Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
1073. ICML 2023Graph Mixup with Soft Alignments
1074. ICML 2023Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
1075. ICML 2023Graph Neural Tangent Kernel: Convergence on Large Graphs
1076. ICML 2023Graph Positional Encoding via Random Feature Propagation
1077. ICML 2023Graph Switching Dynamical Systems
1078. ICML 2023GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
1079. ICML 2023GREAD: Graph Neural Reaction-Diffusion Networks
1080. ICML 2023Half-Hop: A graph upsampling approach for slowing down message passing
1081. ICML 2023Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
1082. ICML 2023HOPE: High-order Graph ODE For Modeling Interacting Dynamics
1083. ICML 2023How much does Initialization Affect Generalization?
1084. ICML 2023Identification of the Adversary from a Single Adversarial Example
1085. ICML 2023Implicit Graph Neural Networks: A Monotone Operator Viewpoint
1086. ICML 2023Improving Graph Generation by Restricting Graph Bandwidth
1087. ICML 2023Improving Graph Neural Networks with Learnable Propagation Operators
1088. ICML 2023InGram: Inductive Knowledge Graph Embedding via Relation Graphs
1089. ICML 2023LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
1090. ICML 2023Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics
1091. ICML 2023Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
1092. ICML 2023Learning Preconditioners for Conjugate Gradient PDE Solvers
1093. ICML 2023Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
1094. ICML 2023Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains
1095. ICML 2023LinSATNet: The Positive Linear Satisfiability Neural Networks
1096. ICML 2023Local Vertex Colouring Graph Neural Networks
1097. ICML 2023Maximal Initial Learning Rates in Deep ReLU Networks
1098. ICML 2023Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
1099. ICML 2023Meta-Learning the Inductive Bias of Simple Neural Circuits
1100. ICML 2023MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods

FAQ

How many Deep learning · graph neural networks papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,648 in total: 728 in 2023, 297 in 2024, 297 in 2025, 326 in 2026.

How many Deep learning · graph neural networks papers are at NeurIPS 2026?

NeurIPS 2026 has 102 Deep learning · graph neural networks papers, versus 91 the year before.

How many Deep learning · graph neural networks papers are at ICLR 2026?

ICLR 2026 has 32 Deep learning · graph neural networks papers, versus 50 the year before.

How many Deep learning · graph neural networks papers are at ICML 2026?

ICML 2026 has 168 Deep learning · graph neural networks papers, versus 141 the year before.

How many Deep learning · graph neural networks papers are at CVPR 2026?

CVPR 2026 has 24 Deep learning · graph neural networks papers, versus 15 the year before.

Which conference publishes the most Deep learning · graph neural networks papers?

NeurIPS, with 842 papers from 2023 to 2026.