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

326 papers, page 6 of 7

251. NeurIPS 2026Cross-Layer Evolution Graph Learning for Fine-grained VLM Hallucination Detection
252. NeurIPS 2026Cross-order Consensus Graph Matching
253. NeurIPS 2026DAG-Biased Graph Learning for Multimodal Survival on EHR
254. NeurIPS 2026DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
255. NeurIPS 2026Distilling Graph Geometry: Knowledge Gap from GNNs to MLPs
256. NeurIPS 2026Do Graph Neural Networks Learn Generalizable Algorithms for Clustering Graphs?
257. NeurIPS 2026DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning
258. NeurIPS 2026Dynamic Regulatory Graph Learning for Histology-to-Spatial Transcriptomics
259. NeurIPS 2026Dynamic Spectral Federated Graph-Level Clustering
260. NeurIPS 2026Faster Dynamic Graph Clustering with Hierarchical Graph Contraction
261. NeurIPS 2026Federated Graph Learning with Local Message Compensation
262. NeurIPS 2026FrED: External Data Influence Estimation via Domain Knowledge Graph Grounding
263. NeurIPS 2026GADMVP: Adaptive Few-Shot Graph-Level Anomaly Detection with Multi-View Structured Prompting
264. NeurIPS 2026GEM: A Dual-Scale Architecture for Graph-Level Hierarchical Representation Learning
265. NeurIPS 2026Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing
266. NeurIPS 2026GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operators
267. NeurIPS 2026Gradient-Free Editing for Attribute Invariance in Graph Neural Networks
268. NeurIPS 2026Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning
269. NeurIPS 2026Graph Label Alignment: A Diagnostic Atlas for Graph Classification
270. NeurIPS 2026Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
271. NeurIPS 2026Graph Topology Augmentation for Prioritized Sweeping in Non-stationary Reinforcement Learning
272. NeurIPS 2026Graph-Based Stochastic-Power-UCT: Monte-Carlo Graph Search with Power Mean Estimation
273. NeurIPS 2026GraphUAT: Uncertainty Attribution in Graph Neural Networks
274. NeurIPS 2026HeterSEED: Semantics–Structure Decoupling for Heterogeneous Graph Learning under Heterophily
275. NeurIPS 2026HiddenPathQA: A Benchmark for Knowledge Graph Question Answering When Questions Hide Their Paths
276. NeurIPS 2026Hierarchical Graph Representation Learning with Pooling-Induced Substructures
277. NeurIPS 2026Hierarchical Regime-Conditioned Dynamics for Spatiotemporal Graphs
278. NeurIPS 2026HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis
279. NeurIPS 2026HoTS: Homophily-Aware Temperature Scaling for Graph Neural Network Calibration
280. NeurIPS 2026IncAgg: Efficient Memory-Enhanced Graph Learning via Incremental Aggregation
281. NeurIPS 2026Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists
282. NeurIPS 2026Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling
283. NeurIPS 2026Invariant Hyperbolic Unfolding: Radial Canonicalization for Label-Free Cross-Graph Link Prediction
284. NeurIPS 2026Learning Minimal Sufficient Evidence Graphs for GraphRAG via Nash-Guided Optimization
285. NeurIPS 2026Learning Pseudo-Riemannian Manifolds for Heterophilic Graphs via Graph Signature
286. NeurIPS 2026Long-Range Spatio-Temporal Graph Propagation Through Oscillations
287. NeurIPS 2026Matrix-Free Stochastic Training of Low-Rank Spectral Graph Learning via Randomized Adaptive Spectral Estimation
288. NeurIPS 2026MCGI: Manifold-Consistent Graph Indexing for Billion-Scale Disk-Resident Vector Search
289. NeurIPS 2026MicroWorld: Empowering Multimodal Large Language Models to Bridge the Microscopic Domain Gap with Multimodal Attribute Graph
290. NeurIPS 2026MIRAGE: Hierarchical MI-Surrogate Regulation for Graph Contrastive Learning
291. NeurIPS 2026MixScentNet: A Multiscale Graph-based Framework for Predicting Scent Mixture Perception
292. NeurIPS 2026Module-Aware Optimization for Graph Neural Networks
293. NeurIPS 2026Multilateral Resistance-Guided Graph Message Passing for Trade Flow Prediction
294. NeurIPS 2026Neighbor-Aware Snapshot-Based Temporal Graph Learning
295. NeurIPS 2026Neutral-atom quantum features as complementary structural encodings for graph learning
296. NeurIPS 2026Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
297. NeurIPS 2026OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
298. NeurIPS 2026On Worst-Case Guarantees for Graph-Based Nearest-Neighbor Search
299. NeurIPS 2026One-Shot Federated Graph Learning via High-Fidelity Proxies and Transferability-Guided Collaboration
300. NeurIPS 2026Orlicz–Sobolev with Musielak: An Efficient Regularization Approach for Graph-based IPM

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.