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 4 of 17

151. NeurIPS 2025Memorization in Graph Neural Networks
152. NeurIPS 2025Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow Variants
153. NeurIPS 2025Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
154. NeurIPS 2025Network two-sample test for block models
155. NeurIPS 2025NeuroH-TGL: Neuro-Heterogeneity Guided Temporal Graph Learning Strategy for Brain Disease Diagnosis
156. NeurIPS 2025Object-Centric Representation Learning for Enhanced 3D Semantic Scene Graph Prediction
157. NeurIPS 2025On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
158. NeurIPS 2025On topological descriptors for graph products
159. NeurIPS 2025One for All: Universal Topological Primitive Transfer for Graph Structure Learning
160. NeurIPS 2025Optimal Graph Clustering without Edge Density Signals
161. NeurIPS 2025Over-squashing in Spatiotemporal Graph Neural Networks
162. NeurIPS 2025Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning
163. NeurIPS 2025ProfiX: Improving Profile-Guided Optimization in Compilers with Graph Neural Networks
164. NeurIPS 2025Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization
165. NeurIPS 2025Pseudo-Riemannian Graph Transformer
166. NeurIPS 2025Refining Norms: A Post-hoc Framework for OOD Detection in Graph Neural Networks
167. NeurIPS 2025Relieving the Over-Aggregating Effect in Graph Transformers
168. NeurIPS 2025Rethinking Tokenized Graph Transformers for Node Classification
169. NeurIPS 2025Revisiting 1-peer exponential graph for enhancing decentralized learning efficiency
170. NeurIPS 2025Revolutionizing Graph Aggregation: From Suppression to Amplification via BoostGCN
171. NeurIPS 2025Robust Graph Condensation via Classification Complexity Mitigation
172. NeurIPS 2025Self-Assembling Graph Perceptrons
173. NeurIPS 2025Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
174. NeurIPS 2025SignFlow Bipartite Subgraph Network For Large-Scale Graph Link Sign Prediction
175. NeurIPS 2025Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
176. NeurIPS 2025SONAR: Long-Range Graph Propagation Through Information Waves
177. NeurIPS 2025Sound Logical Explanations for Mean Aggregation Graph Neural Networks
178. NeurIPS 2025Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum
179. NeurIPS 2025Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
180. NeurIPS 2025TAMI: Taming Heterogeneity in Temporal Interactions for Temporal Graph Link Prediction
181. NeurIPS 2025Taxonomy of reduction matrices for Graph Coarsening
182. NeurIPS 2025The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics
183. NeurIPS 2025The quest for the GRAph Level autoEncoder (GRALE)
184. NeurIPS 2025TopER: Topological Embeddings in Graph Representation Learning
185. NeurIPS 2025Topology-aware Graph Diffusion Model with Persistent Homology
186. NeurIPS 2025Topology-Aware Learning of Tubular Manifolds via SE(3)-Equivariant Network on Ball B-Spline Curve
187. NeurIPS 2025Towards Generalizable Retina Vessel Segmentation with Deformable Graph Priors
188. NeurIPS 2025Towards Pre-trained Graph Condensation via Optimal Transport
189. NeurIPS 2025Uncertain Knowledge Graph Completion via Semi-Supervised Confidence Distribution Learning
190. NeurIPS 2025Universally Invariant Learning in Equivariant GNNs
191. NeurIPS 2025Unsupervised Federated Graph Learning
192. NeurIPS 2025VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold
193. NeurIPS 2025You Only Spectralize Once: Taking a Spectral Detour to Accelerate Graph Neural Network
194. NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
195. NeurIPS 2024[Re] Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework"
196. NeurIPS 2024A Canonicalization Perspective on Invariant and Equivariant Learning
197. NeurIPS 2024A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening
198. NeurIPS 2024A Foundation Model for Zero-shot Logical Query Reasoning
199. NeurIPS 2024A Motion-aware Spatio-temporal Graph for Video Salient Object Ranking
200. NeurIPS 2024A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoning

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.