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

297 papers, page 6 of 6

251. NeurIPS 2025Local-Global Coupling Spiking Graph Transformer for Brain Disorders Diagnosis from Two Perspectives
252. NeurIPS 2025Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization
253. NeurIPS 2025Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness–Generalization Perspective
254. NeurIPS 2025MaNGO — Adaptable Graph Network Simulators via Meta-Learning
255. NeurIPS 2025Memorization in Graph Neural Networks
256. NeurIPS 2025Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow Variants
257. NeurIPS 2025Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
258. NeurIPS 2025Network two-sample test for block models
259. NeurIPS 2025NeuroH-TGL: Neuro-Heterogeneity Guided Temporal Graph Learning Strategy for Brain Disease Diagnosis
260. NeurIPS 2025Object-Centric Representation Learning for Enhanced 3D Semantic Scene Graph Prediction
261. NeurIPS 2025On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
262. NeurIPS 2025On topological descriptors for graph products
263. NeurIPS 2025One for All: Universal Topological Primitive Transfer for Graph Structure Learning
264. NeurIPS 2025Optimal Graph Clustering without Edge Density Signals
265. NeurIPS 2025Over-squashing in Spatiotemporal Graph Neural Networks
266. NeurIPS 2025Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning
267. NeurIPS 2025ProfiX: Improving Profile-Guided Optimization in Compilers with Graph Neural Networks
268. NeurIPS 2025Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization
269. NeurIPS 2025Pseudo-Riemannian Graph Transformer
270. NeurIPS 2025Refining Norms: A Post-hoc Framework for OOD Detection in Graph Neural Networks
271. NeurIPS 2025Relieving the Over-Aggregating Effect in Graph Transformers
272. NeurIPS 2025Rethinking Tokenized Graph Transformers for Node Classification
273. NeurIPS 2025Revisiting 1-peer exponential graph for enhancing decentralized learning efficiency
274. NeurIPS 2025Revolutionizing Graph Aggregation: From Suppression to Amplification via BoostGCN
275. NeurIPS 2025Robust Graph Condensation via Classification Complexity Mitigation
276. NeurIPS 2025Self-Assembling Graph Perceptrons
277. NeurIPS 2025Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
278. NeurIPS 2025SignFlow Bipartite Subgraph Network For Large-Scale Graph Link Sign Prediction
279. NeurIPS 2025Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
280. NeurIPS 2025SONAR: Long-Range Graph Propagation Through Information Waves
281. NeurIPS 2025Sound Logical Explanations for Mean Aggregation Graph Neural Networks
282. NeurIPS 2025Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum
283. NeurIPS 2025Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
284. NeurIPS 2025TAMI: Taming Heterogeneity in Temporal Interactions for Temporal Graph Link Prediction
285. NeurIPS 2025Taxonomy of reduction matrices for Graph Coarsening
286. NeurIPS 2025The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics
287. NeurIPS 2025The quest for the GRAph Level autoEncoder (GRALE)
288. NeurIPS 2025TopER: Topological Embeddings in Graph Representation Learning
289. NeurIPS 2025Topology-aware Graph Diffusion Model with Persistent Homology
290. NeurIPS 2025Topology-Aware Learning of Tubular Manifolds via SE(3)-Equivariant Network on Ball B-Spline Curve
291. NeurIPS 2025Towards Generalizable Retina Vessel Segmentation with Deformable Graph Priors
292. NeurIPS 2025Towards Pre-trained Graph Condensation via Optimal Transport
293. NeurIPS 2025Uncertain Knowledge Graph Completion via Semi-Supervised Confidence Distribution Learning
294. NeurIPS 2025Universally Invariant Learning in Equivariant GNNs
295. NeurIPS 2025Unsupervised Federated Graph Learning
296. NeurIPS 2025VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold
297. NeurIPS 2025You Only Spectralize Once: Taking a Spectral Detour to Accelerate Graph Neural Network

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.