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 5 of 10

201. NeurIPS 2023GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
202. NeurIPS 2023Goal-conditioned Offline Planning from Curious Exploration
203. NeurIPS 2023Going beyond persistent homology using persistent homology
204. NeurIPS 2023GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
205. NeurIPS 2023Gradient-Based Feature Learning under Structured Data
206. NeurIPS 2023Graph Clustering with Graph Neural Networks
207. NeurIPS 2023Graph Contrastive Learning with Stable and Scalable Spectral Encoding
208. NeurIPS 2023Graph Convolutional Kernel Machine versus Graph Convolutional Networks
209. NeurIPS 2023Graph Denoising Diffusion for Inverse Protein Folding
210. NeurIPS 2023Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
211. NeurIPS 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis
212. NeurIPS 2023Graph of Circuits with GNN for Exploring the Optimal Design Space
213. NeurIPS 2023Graph-Structured Gaussian Processes for Transferable Graph Learning
214. NeurIPS 2023GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search
215. NeurIPS 2023GSLB: The Graph Structure Learning Benchmark
216. NeurIPS 2023GUST: Combinatorial Generalization by Unsupervised Grouping with Neuronal Coherence
217. NeurIPS 2023Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition
218. NeurIPS 2023Hierarchical Randomized Smoothing
219. NeurIPS 2023High dimensional, tabular deep learning with an auxiliary knowledge graph
220. NeurIPS 2023HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
221. NeurIPS 2023How Does Adaptive Optimization Impact Local Neural Network Geometry?
222. NeurIPS 2023HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
223. NeurIPS 2023Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach
224. NeurIPS 2023Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
225. NeurIPS 2023Hypervolume Maximization: A Geometric View of Pareto Set Learning
226. NeurIPS 2023Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network
227. NeurIPS 2023Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data
228. NeurIPS 2023Implicit Manifold Gaussian Process Regression
229. NeurIPS 2023Implicit Transfer Operator Learning: Multiple Time-Resolution Models for Molecular Dynamics
230. NeurIPS 2023Improvements on Uncertainty Quantification for Node Classification via Distance Based Regularization
231. NeurIPS 2023Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings
232. NeurIPS 2023Improving Graph Matching with Positional Reconstruction Encoder-Decoder Network
233. NeurIPS 2023Improving neural network representations using human similarity judgments
234. NeurIPS 2023Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training
235. NeurIPS 2023Individualized Dosing Dynamics via Neural Eigen Decomposition
236. NeurIPS 2023Information Geometry of the Retinal Representation Manifold
237. NeurIPS 2023Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks
238. NeurIPS 2023Inner Product-based Neural Network Similarity
239. NeurIPS 2023Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
240. NeurIPS 2023Interpretable Graph Networks Formulate Universal Algebra Conjectures
241. NeurIPS 2023Interpretable Prototype-based Graph Information Bottleneck
242. NeurIPS 2023Investigating how ReLU-networks encode symmetries
243. NeurIPS 2023Is Distance Matrix Enough for Geometric Deep Learning?
244. NeurIPS 2023Joint Training of Deep Ensembles Fails Due to Learner Collusion
245. NeurIPS 2023KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training
246. NeurIPS 2023Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
247. NeurIPS 2023Labeling Neural Representations with Inverse Recognition
248. NeurIPS 2023LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
249. NeurIPS 2023Language Semantic Graph Guided Data-Efficient Learning
250. NeurIPS 2023Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding

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.