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 7 of 15

301. NeurIPS 2023Binary Radiance Fields
302. NeurIPS 2023Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training
303. NeurIPS 2023Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization
304. NeurIPS 2023Boosting Learning for LDPC Codes to Improve the Error-Floor Performance
305. NeurIPS 2023Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks
306. NeurIPS 2023BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
307. NeurIPS 2023Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing
308. NeurIPS 2023Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs
309. NeurIPS 2023CAT-Walk: Inductive Hypergraph Learning via Set Walks
310. NeurIPS 2023Causes and Effects of Unanticipated Numerical Deviations in Neural Network Inference Frameworks
311. NeurIPS 2023CBD: A Certified Backdoor Detector Based on Local Dominant Probability
312. NeurIPS 2023Certifiably Robust Graph Contrastive Learning
313. NeurIPS 2023Certification of Distributional Individual Fairness
314. NeurIPS 2023Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
315. NeurIPS 2023Cognitive Model Discovery via Disentangled RNNs
316. NeurIPS 2023Cognitive Steering in Deep Neural Networks via Long-Range Modulatory Feedback Connections
317. NeurIPS 2023Combating Bilateral Edge Noise for Robust Link Prediction
318. NeurIPS 2023Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
319. NeurIPS 2023Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
320. NeurIPS 2023Compression with Bayesian Implicit Neural Representations
321. NeurIPS 2023Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
322. NeurIPS 2023Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data
323. NeurIPS 2023Conformal Prediction for Time Series with Modern Hopfield Networks
324. NeurIPS 2023Contextual Gaussian Process Bandits with Neural Networks
325. NeurIPS 2023Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
326. NeurIPS 2023Convergence of Actor-Critic with Multi-Layer Neural Networks
327. NeurIPS 2023Convolutional Neural Operators for robust and accurate learning of PDEs
328. NeurIPS 2023Coop: Memory is not a Commodity
329. NeurIPS 2023CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics
330. NeurIPS 2023Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry
331. NeurIPS 2023CosNet: A Generalized Spectral Kernel Network
332. NeurIPS 2023CP-SLAM: Collaborative Neural Point-based SLAM System
333. NeurIPS 2023Crystal Structure Prediction by Joint Equivariant Diffusion
334. NeurIPS 2023CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
335. NeurIPS 2023Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First
336. NeurIPS 2023Curvature Filtrations for Graph Generative Model Evaluation
337. NeurIPS 2023D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion
338. NeurIPS 2023Data-Centric Learning from Unlabeled Graphs with Diffusion Model
339. NeurIPS 2023Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
340. NeurIPS 2023Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
341. NeurIPS 2023Deep Insights into Noisy Pseudo Labeling on Graph Data
342. NeurIPS 2023Deep learning with kernels through RKHM and the Perron-Frobenius operator
343. NeurIPS 2023Deep Patch Visual Odometry
344. NeurIPS 2023Deep Recurrent Optimal Stopping
345. NeurIPS 2023Deep Stochastic Processes via Functional Markov Transition Operators
346. NeurIPS 2023DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
347. NeurIPS 2023DeepPCR: Parallelizing Sequential Operations in Neural Networks
348. NeurIPS 2023Degraded Polygons Raise Fundamental Questions of Neural Network Perception
349. NeurIPS 2023Demystifying Oversmoothing in Attention-Based Graph Neural Networks
350. NeurIPS 2023Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?

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.