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

24 papers, page 1 of 1

1. CVPR 2026Adaptive Learned Image Compression with Graph Neural Networks
2. CVPR 2026Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization
3. CVPR 2026Beyond Graph Model Reliable VLM Fine-Tuning via Random Graph Adapter
4. CVPR 2026BrepVGAE Variational Graph Autoencoder with Unified Latent Representation for B-rep
5. CVPR 2026BrickNet Graph-Backed Generative Brick Assembly
6. CVPR 2026CIGPose Causal Intervention Graph Neural Network for Whole-Body Pose Estimation
7. CVPR 2026Cross-Slice Knowledge Transfer via Masked Multi-Modal Heterogeneous Graph Contrastive Learning
8. CVPR 2026Dual Graph Regularized Deep Unfolding Network for Guided Depth Map
9. CVPR 2026DynamicGTR Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities
10. CVPR 2026FedSST Rethinking Fair Federated Graph Learning under Structural Shift
11. CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
12. CVPR 2026Global-Aware Edge Prioritization for Pose Graph Initialization
13. CVPR 2026Graph Attention Prototypical Network for Robust Few-Shot Classification
14. CVPR 2026GraPHFormer A Multimodal Graph Persistent Homology Transformer for the Analysis
15. CVPR 2026GSNR Graph Smooth Null-Space Representation for Inverse Problems
16. CVPR 2026Learning Scene Coordinate Reconstruction from Unposed Images via Pose Graph
17. CVPR 2026PP-Brep Few-Shot B-rep Classification with Hybrid Graph Representation
18. CVPR 2026RoboTAG End-to-end Robot Pose Estimation via Topological Alignment Graph
19. CVPR 2026Semantic Derivative Flow Graph-Guided Diffusion for Controllable Instance Interactions
20. CVPR 2026Solvability of the Viewing Graph Under the Affine Camera Model
21. CVPR 2026STRNet Visual Navigation with Spatio-Temporal Representation through Dynamic Graph Aggregation
22. CVPR 2026Structural Graph Probing of Vision-Language Models
23. CVPR 2026Variational Graph-based Normal Integration
24. CVPR 2026Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor

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.