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

842 papers, page 15 of 17

701. NeurIPS 2023Optimal Block-wise Asymmetric Graph Construction for Graph-based Semi-supervised Learning
702. NeurIPS 2023Optimality of Message-Passing Architectures for Sparse Graphs
703. NeurIPS 2023Optimization and Bayes: A Trade-off for Overparameterized Neural Networks
704. NeurIPS 2023Outlier-Robust Gromov-Wasserstein for Graph Data
705. NeurIPS 2023Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks
706. NeurIPS 2023PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
707. NeurIPS 2023Penalising the biases in norm regularisation enforces sparsity
708. NeurIPS 2023PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis
709. NeurIPS 2023Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex
710. NeurIPS 2023Permutation Equivariant Neural Functionals
711. NeurIPS 2023PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems
712. NeurIPS 2023Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width
713. NeurIPS 2023Polyhedron Attention Module: Learning Adaptive-order Interactions
714. NeurIPS 2023Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets
715. NeurIPS 2023Posthoc privacy guarantees for collaborative inference with modified Propose-Test-Release
716. NeurIPS 2023Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily
717. NeurIPS 2023Probabilistic Invariant Learning with Randomized Linear Classifiers
718. NeurIPS 2023Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantisation.
719. NeurIPS 2023Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs
720. NeurIPS 2023Provable Guarantees for Neural Networks via Gradient Feature Learning
721. NeurIPS 2023Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks
722. NeurIPS 2023Provable Training for Graph Contrastive Learning
723. NeurIPS 2023Provably Bounding Neural Network Preimages
724. NeurIPS 2023Pruning vs Quantization: Which is Better?
725. NeurIPS 2023Public Opinion Field Effect Fusion in Representation Learning for Trending Topics Diffusion
726. NeurIPS 2023PyNeRF: Pyramidal Neural Radiance Fields
727. NeurIPS 2023QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
728. NeurIPS 2023QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution
729. NeurIPS 2023Quasi-Monte Carlo Graph Random Features
730. NeurIPS 2023Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks
731. NeurIPS 2023Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
732. NeurIPS 2023Recurrent Temporal Revision Graph Networks
733. NeurIPS 2023Red Teaming Deep Neural Networks with Feature Synthesis Tools
734. NeurIPS 2023Regularizing Neural Networks with Meta-Learning Generative Models
735. NeurIPS 2023Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning
736. NeurIPS 2023ResMem: Learn what you can and memorize the rest
737. NeurIPS 2023Retaining Beneficial Information from Detrimental Data for Neural Network Repair
738. NeurIPS 2023Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
739. NeurIPS 2023Rethinking the Backward Propagation for Adversarial Transferability
740. NeurIPS 2023Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules
741. NeurIPS 2023Reversible and irreversible bracket-based dynamics for deep graph neural networks
742. NeurIPS 2023Riemannian Laplace approximations for Bayesian neural networks
743. NeurIPS 2023Riemannian Residual Neural Networks
744. NeurIPS 2023Robust low-rank training via approximate orthonormal constraints
745. NeurIPS 2023Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model
746. NeurIPS 2023Robustness Guarantees for Adversarially Trained Neural Networks
747. NeurIPS 2023Saddle-to-Saddle Dynamics in Diagonal Linear Networks
748. NeurIPS 2023Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks
749. NeurIPS 2023Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
750. NeurIPS 2023Scale Alone Does not Improve Mechanistic Interpretability in Vision Models

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.