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

533 papers, page 10 of 11

451. ICML 2023Graph Switching Dynamical Systems
452. ICML 2023GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
453. ICML 2023GREAD: Graph Neural Reaction-Diffusion Networks
454. ICML 2023Half-Hop: A graph upsampling approach for slowing down message passing
455. ICML 2023Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
456. ICML 2023HOPE: High-order Graph ODE For Modeling Interacting Dynamics
457. ICML 2023How much does Initialization Affect Generalization?
458. ICML 2023Identification of the Adversary from a Single Adversarial Example
459. ICML 2023Implicit Graph Neural Networks: A Monotone Operator Viewpoint
460. ICML 2023Improving Graph Generation by Restricting Graph Bandwidth
461. ICML 2023Improving Graph Neural Networks with Learnable Propagation Operators
462. ICML 2023InGram: Inductive Knowledge Graph Embedding via Relation Graphs
463. ICML 2023LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
464. ICML 2023Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics
465. ICML 2023Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
466. ICML 2023Learning Preconditioners for Conjugate Gradient PDE Solvers
467. ICML 2023Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
468. ICML 2023Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains
469. ICML 2023LinSATNet: The Positive Linear Satisfiability Neural Networks
470. ICML 2023Local Vertex Colouring Graph Neural Networks
471. ICML 2023Maximal Initial Learning Rates in Deep ReLU Networks
472. ICML 2023Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
473. ICML 2023Meta-Learning the Inductive Bias of Simple Neural Circuits
474. ICML 2023MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
475. ICML 2023Minimal Width for Universal Property of Deep RNN
476. ICML 2023Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
477. ICML 2023Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
478. ICML 2023Moccasin: Efficient Tensor Rematerialization for Neural Networks
479. ICML 2023Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
480. ICML 2023MODeL: Memory Optimizations for Deep Learning
481. ICML 2023Multi-class Graph Clustering via Approximated Effective $p$-Resistance
482. ICML 2023Neural networks trained with SGD learn distributions of increasing complexity
483. ICML 2023Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks
484. ICML 2023NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes
485. ICML 2023Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
486. ICML 2023On the Connection Between MPNN and Graph Transformer
487. ICML 2023On the Expressive Power of Geometric Graph Neural Networks
488. ICML 2023On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization
489. ICML 2023On the Initialization of Graph Neural Networks
490. ICML 2023Optimizing NOTEARS Objectives via Topological Swaps
491. ICML 2023Overcoming Simplicity Bias in Deep Networks using a Feature Sieve
492. ICML 2023Path Neural Networks: Expressive and Accurate Graph Neural Networks
493. ICML 2023Personalized Subgraph Federated Learning
494. ICML 2023Perturbation Analysis of Neural Collapse
495. ICML 2023Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
496. ICML 2023Quantitative Universal Approximation Bounds for Deep Belief Networks
497. ICML 2023Randomized Schur Complement Views for Graph Contrastive Learning
498. ICML 2023Relevant Walk Search for Explaining Graph Neural Networks
499. ICML 2023Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
500. ICML 2023Revisiting Structured Variational Autoencoders

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.