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

489 papers, page 7 of 10

301. NeurIPS 2023Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives
302. NeurIPS 2023Nearly Tight Bounds For Differentially Private Multiway Cut
303. NeurIPS 2023NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks
304. NeurIPS 2023Network Regression with Graph Laplacians
305. NeurIPS 2023Networks are Slacking Off: Understanding Generalization Problem in Image Deraining
306. NeurIPS 2023Neural (Tangent Kernel) Collapse
307. NeurIPS 2023Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions
308. NeurIPS 2023Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
309. NeurIPS 2023Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity
310. NeurIPS 2023Neural Functional Transformers
311. NeurIPS 2023Neural Graph Generation from Graph Statistics
312. NeurIPS 2023Neural Injective Functions for Multisets, Measures and Graphs via a Finite Witness Theorem
313. NeurIPS 2023Neural Lad: A Neural Latent Dynamics Framework for Times Series Modeling
314. NeurIPS 2023Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization
315. NeurIPS 2023Neural Lyapunov Control for Discrete-Time Systems
316. NeurIPS 2023Neural Modulation for Flash Memory: An Unsupervised Learning Framework for Improved Reliability
317. NeurIPS 2023Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
318. NeurIPS 2023Neural Oscillators are Universal
319. NeurIPS 2023Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features
320. NeurIPS 2023Neural Priming for Sample-Efficient Adaptation
321. NeurIPS 2023Neural Processes with Stability
322. NeurIPS 2023Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data
323. NeurIPS 2023Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis
324. NeurIPS 2023NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function
325. NeurIPS 2023Neuro-symbolic Learning Yielding Logical Constraints
326. NeurIPS 2023NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
327. NeurIPS 2023Newton–Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
328. NeurIPS 2023No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning
329. NeurIPS 2023Nonparametric Boundary Geometry in Physics Informed Deep Learning
330. NeurIPS 2023Norm-based Generalization Bounds for Sparse Neural Networks
331. NeurIPS 2023Normalization-Equivariant Neural Networks with Application to Image Denoising
332. NeurIPS 2023Normalizing flow neural networks by JKO scheme
333. NeurIPS 2023NPCL: Neural Processes for Uncertainty-Aware Continual Learning
334. NeurIPS 2023NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos
335. NeurIPS 2023ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
336. NeurIPS 2023On Single-Index Models beyond Gaussian Data
337. NeurIPS 2023On the Ability of Graph Neural Networks to Model Interactions Between Vertices
338. NeurIPS 2023On the Asymptotic Learning Curves of Kernel Ridge Regression under Power-law Decay
339. NeurIPS 2023On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $\epsilon$-Greedy Exploration
340. NeurIPS 2023On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
341. NeurIPS 2023On the Implicit Bias of Linear Equivariant Steerable Networks
342. NeurIPS 2023On the Overlooked Structure of Stochastic Gradients
343. NeurIPS 2023On the Pareto Front of Multilingual Neural Machine Translation
344. NeurIPS 2023On the Role of Randomization in Adversarially Robust Classification
345. NeurIPS 2023On the spectral bias of two-layer linear networks
346. NeurIPS 2023OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
347. NeurIPS 2023Operator Learning with Neural Fields: Tackling PDEs on General Geometries
348. NeurIPS 2023Optimal Block-wise Asymmetric Graph Construction for Graph-based Semi-supervised Learning
349. NeurIPS 2023Optimality of Message-Passing Architectures for Sparse Graphs
350. NeurIPS 2023Optimization and Bayes: A Trade-off for Overparameterized Neural Networks

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.