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 11 of 17

501. NeurIPS 2023Entropic Neural Optimal Transport via Diffusion Processes
502. NeurIPS 2023Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs
503. NeurIPS 2023Episodic Multi-Task Learning with Heterogeneous Neural Processes
504. NeurIPS 2023Epistemic Neural Networks
505. NeurIPS 2023Equivariant Neural Operator Learning with Graphon Convolution
506. NeurIPS 2023Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
507. NeurIPS 2023ESSEN: Improving Evolution State Estimation for Temporal Networks using Von Neumann Entropy
508. NeurIPS 2023Euler-Lagrange Analysis of Generative Adversarial Networks
509. NeurIPS 2023Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
510. NeurIPS 2023Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis
511. NeurIPS 2023Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
512. NeurIPS 2023Evolving Connectivity for Recurrent Spiking Neural Networks
513. NeurIPS 2023Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
514. NeurIPS 2023Exact Verification of ReLU Neural Control Barrier Functions
515. NeurIPS 2023Explain Any Concept: Segment Anything Meets Concept-Based Explanation
516. NeurIPS 2023Explainable Brain Age Prediction using coVariance Neural Networks
517. NeurIPS 2023Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
518. NeurIPS 2023Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
519. NeurIPS 2023Expressive probabilistic sampling in recurrent neural networks
520. NeurIPS 2023Expressive Sign Equivariant Networks for Spectral Geometric Learning
521. NeurIPS 2023Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
522. NeurIPS 2023Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
523. NeurIPS 2023Fair Graph Distillation
524. NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
525. NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
526. NeurIPS 2023Federated Learning via Meta-Variational Dropout
527. NeurIPS 2023FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
528. NeurIPS 2023Fine-grained Expressivity of Graph Neural Networks
529. NeurIPS 2023Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs
530. NeurIPS 2023Flat Seeking Bayesian Neural Networks
531. NeurIPS 2023FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow
532. NeurIPS 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
533. NeurIPS 2023FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow
534. NeurIPS 2023Fragment-based Pretraining and Finetuning on Molecular Graphs
535. NeurIPS 2023From Tempered to Benign Overfitting in ReLU Neural Networks
536. NeurIPS 2023Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
537. NeurIPS 2023Full-Atom Protein Pocket Design via Iterative Refinement
538. NeurIPS 2023Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
539. NeurIPS 2023Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
540. NeurIPS 2023Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
541. NeurIPS 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
542. NeurIPS 2023GALOPA: Graph Transport Learning with Optimal Plan Alignment
543. NeurIPS 2023Generalised f-Mean Aggregation for Graph Neural Networks
544. NeurIPS 2023Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
545. NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
546. NeurIPS 2023Generating Behaviorally Diverse Policies with Latent Diffusion Models
547. NeurIPS 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEs
548. NeurIPS 2023GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
549. NeurIPS 2023GEQ: Gaussian Kernel Inspired Equilibrium Models
550. NeurIPS 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning

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.