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

728 papers, page 13 of 15

601. NeurIPS 2023Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release
602. NeurIPS 2023Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily
603. NeurIPS 2023Probabilistic Invariant Learning with Randomized Linear Classifiers
604. NeurIPS 2023Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantisation.
605. NeurIPS 2023Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs
606. NeurIPS 2023Provable Guarantees for Neural Networks via Gradient Feature Learning
607. NeurIPS 2023Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
608. NeurIPS 2023Provable Training for Graph Contrastive Learning
609. NeurIPS 2023Provably Bounding Neural Network Preimages
610. NeurIPS 2023Pruning vs Quantization: Which is Better?
611. NeurIPS 2023Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion
612. NeurIPS 2023PyNeRF: Pyramidal Neural Radiance Fields
613. NeurIPS 2023QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
614. NeurIPS 2023QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution
615. NeurIPS 2023Quasi-Monte Carlo Graph Random Features
616. NeurIPS 2023Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks
617. NeurIPS 2023Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
618. NeurIPS 2023Recurrent Temporal Revision Graph Networks
619. NeurIPS 2023Red Teaming Deep Neural Networks with Feature Synthesis Tools
620. NeurIPS 2023Regularizing Neural Networks with Meta-Learning Generative Models
621. NeurIPS 2023Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
622. NeurIPS 2023ResMem: Learn what you can and memorize the rest
623. NeurIPS 2023Retaining Beneficial Information from Detrimental Data for Neural Network Repair
624. NeurIPS 2023Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
625. NeurIPS 2023Rethinking the Backward Propagation for Adversarial Transferability
626. NeurIPS 2023Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules
627. NeurIPS 2023Reversible and irreversible bracket-based dynamics for deep graph neural networks
628. NeurIPS 2023Riemannian Laplace approximations for Bayesian neural networks
629. NeurIPS 2023Riemannian Residual Neural Networks
630. NeurIPS 2023Robust low-rank training via approximate orthonormal constraints
631. NeurIPS 2023Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model
632. NeurIPS 2023Robustness Guarantees for Adversarially Trained Neural Networks
633. NeurIPS 2023Saddle-to-Saddle Dynamics in Diagonal Linear Networks
634. NeurIPS 2023Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
635. NeurIPS 2023Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
636. NeurIPS 2023Scale Alone Does not Improve Mechanistic Interpretability in Vision Models
637. NeurIPS 2023Scale-Space Hypernetworks for Efficient Biomedical Image Analysis
638. NeurIPS 2023Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels
639. NeurIPS 2023Scaling Laws for Hyperparameter Optimization
640. NeurIPS 2023Self-supervised Graph Neural Networks via Low-Rank Decomposition
641. NeurIPS 2023Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
642. NeurIPS 2023Separable Physics-Informed Neural Networks
643. NeurIPS 2023SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
644. NeurIPS 2023Sharpness-Aware Minimization Leads to Low-Rank Features
645. NeurIPS 2023Sheaf Hypergraph Networks
646. NeurIPS 2023Should Under-parameterized Student Networks Copy or Average Teacher Weights?
647. NeurIPS 2023Simple and Asymmetric Graph Contrastive Learning without Augmentations
648. NeurIPS 2023Simplicity Bias in 1-Hidden Layer Neural Networks
649. NeurIPS 2023Simplifying Neural Network Training Under Class Imbalance
650. NeurIPS 2023Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization

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.