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 1 of 17

1. NeurIPS 2026A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry
2. NeurIPS 2026A Graph Foundation Model for Unified Clustering
3. NeurIPS 2026A New Perspective on Target-Conditioned Structural Dynamics for Link Prediction in Dynamic Graphs
4. NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
5. NeurIPS 2026A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion
6. NeurIPS 2026A2I: Adjacency-to-Image Structural Encodings for Graph Learning
7. NeurIPS 2026Adaptive Feature Propagation for Attribute-Missing Graph Clustering
8. NeurIPS 2026Adaptive Gated Simplicial Propagation for Node Classification in Multimodal Graphs
9. NeurIPS 2026Adaptive Multi-view Graph Contrastive Learning via Fractional Continuous Dynamics
10. NeurIPS 2026Aggregation Dispersion: An Information-Geometric Diagnostic of Oversmoothing in Graph Neural Networks
11. NeurIPS 2026Approximation of Maximally Monotone Operators : A Graph Convergence Perspective
12. NeurIPS 2026Avoiding Feature Collapse in Graph ODEs via Hysteretic Topology Evolution
13. NeurIPS 2026B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
14. NeurIPS 2026Benchmarking Graph Self-Supervised Learning for Node-Level Tasks: Insights and Strong Baseline
15. NeurIPS 2026Beyond Adjacent Layers: Graph-Guided Layer Fusion for Compressing Large Language Models
16. NeurIPS 2026Beyond Node Sequences: Relational Diffusion for Unified Graph Learning
17. NeurIPS 2026Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
18. NeurIPS 2026Biological Graph Priors Enable Representation Learning for Cellular Microscopy
19. NeurIPS 2026Boosting Graph Contrastive Learning via Manifold-Guided Representation Disentanglement
20. NeurIPS 2026Bridging Graph Worlds: Neural Approximation of Gromov-Wasserstein Distances
21. NeurIPS 2026Caterpillar GNN: Replacing Message Passing with Graph-Level Aggregation
22. NeurIPS 2026Chaining 2-FWL GNNs for Combinatorial Graph Alignment
23. NeurIPS 2026CHORD: Cross-Model Hallucination Detection via Relational Graph Discrimination
24. NeurIPS 2026CommunityKV: Efficient Long-Context Decoding via Graph Partitioning
25. NeurIPS 2026Contractive Monoids: The Algebra Behind Stable Residual Propagation in Deep Graph Neural Networks
26. NeurIPS 2026Contrastive Adversarial Training for Robust Graph Neural Networks under Label Poisoning
27. NeurIPS 2026Cross-Layer Evolution Graph Learning for Fine-grained VLM Hallucination Detection
28. NeurIPS 2026Cross-order Consensus Graph Matching
29. NeurIPS 2026DAG-Biased Graph Learning for Multimodal Survival on EHR
30. NeurIPS 2026DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
31. NeurIPS 2026Distilling Graph Geometry: Knowledge Gap from GNNs to MLPs
32. NeurIPS 2026Do Graph Neural Networks Learn Generalizable Algorithms for Clustering Graphs?
33. NeurIPS 2026DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning
34. NeurIPS 2026Dynamic Regulatory Graph Learning for Histology-to-Spatial Transcriptomics
35. NeurIPS 2026Dynamic Spectral Federated Graph-Level Clustering
36. NeurIPS 2026Faster Dynamic Graph Clustering with Hierarchical Graph Contraction
37. NeurIPS 2026Federated Graph Learning with Local Message Compensation
38. NeurIPS 2026FrED: External Data Influence Estimation via Domain Knowledge Graph Grounding
39. NeurIPS 2026GADMVP: Adaptive Few-Shot Graph-Level Anomaly Detection with Multi-View Structured Prompting
40. NeurIPS 2026GEM: A Dual-Scale Architecture for Graph-Level Hierarchical Representation Learning
41. NeurIPS 2026Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing
42. NeurIPS 2026GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operators
43. NeurIPS 2026Gradient-Free Editing for Attribute Invariance in Graph Neural Networks
44. NeurIPS 2026Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning
45. NeurIPS 2026Graph Label Alignment: A Diagnostic Atlas for Graph Classification
46. NeurIPS 2026Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
47. NeurIPS 2026Graph Topology Augmentation for Prioritized Sweeping in Non-stationary Reinforcement Learning
48. NeurIPS 2026Graph-Based Stochastic-Power-UCT: Monte-Carlo Graph Search with Power Mean Estimation
49. NeurIPS 2026GraphUAT: Uncertainty Attribution in Graph Neural Networks
50. NeurIPS 2026HeterSEED: Semantics–Structure Decoupling for Heterogeneous Graph Learning under Heterophily

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.