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

201 papers, page 1 of 5

1. ICLR 2026A Function-Centric Graph Neural Network Approach for Predicting Electron Densities
2. ICLR 2026AdS-GNN - a Conformally Equivariant Graph Neural Network
3. ICLR 2026Any-Subgroup Equivariant Networks via Symmetry Breaking
4. ICLR 2026Certified Evaluation of Model-Level Explanations for Graph Neural Networks
5. ICLR 2026Confident Block Diagonal Structure-Aware Invariable Graph Completion for Incomplete Multi-view Clustering
6. ICLR 2026Dynamic Kernel Graph Sparsifiers
7. ICLR 2026Escaping the Homophily Trap: A Threshold-free Graph Outlier Detection Framework via Clustering-guided Edge Reweighting
8. ICLR 2026Federated Graph-Level Clustering Network with Dual Knowledge Separation
9. ICLR 2026Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
10. ICLR 2026Forest-Based Graph Learning for Semi-Supervised Node Classification
11. ICLR 2026Graph homophily booster: Reimagining the role of discrete features in heterophilic graph learning
12. ICLR 2026Graph Mixing Additive Networks
13. ICLR 2026Graph Representational Learning: When Does More Expressivity Hurt Generalization?
14. ICLR 2026Graph Signal Processing Meets Mamba2: Adaptive Filter Bank via Delta Modulation
15. ICLR 2026Graph-based Nearest Neighbors with Dynamic Updates via Random Walks
16. ICLR 2026Graph-Theoretic Intrinsic Reward: Guiding RL with Effective Resistance
17. ICLR 2026GTool: Graph Enhanced Tool Planning with Large Language Model
18. ICLR 2026Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics
19. ICLR 2026Laplacian Kernelized Bandit
20. ICLR 2026Learning from Historical Activations in Graph Neural Networks
21. ICLR 2026Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrolling
22. ICLR 2026Multi-Scale Diffusion-Guided Graph Learning with Power-Smoothing Random Walk Contrast for Multi-View Clustering
23. ICLR 2026On The Expressive Power of GNN Derivatives
24. ICLR 2026One for Two: A Unified Framework for Imbalanced Graph Classification via Dynamic Balanced Prototype
25. ICLR 2026Out-of-Distribution Graph Models Merging
26. ICLR 2026ProofFlow: A Dependency Graph Approach to Faithful Proof Autoformalization
27. ICLR 2026Rapid Training of Hamiltonian Graph Networks Using Random Features
28. ICLR 2026Relational Graph Transformer
29. ICLR 2026Relatron: Automating Relational Machine Learning over Relational Databases
30. ICLR 2026Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
31. ICLR 2026Temporal Graph Thumbnail: Robust Representation Learning with Global Evolutionary Skeleton
32. ICLR 2026TetraGT: Tetrahedral Geometry-Driven Explicit Token Interactions with Graph Transformer for Molecular Representation Learning
33. ICLR 2025Beyond Circuit Connections: A Non-Message Passing Graph Transformer Approach for Quantum Error Mitigation
34. ICLR 2025Beyond Random Masking: When Dropout meets Graph Convolutional Networks
35. ICLR 2025Bias Mitigation in Graph Diffusion Models
36. ICLR 2025Biologically Plausible Brain Graph Transformer
37. ICLR 2025BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
38. ICLR 2025Centrality-guided Pre-training for Graph
39. ICLR 2025Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs
40. ICLR 2025Deep Signature: Characterization of Large-Scale Molecular Dynamics
41. ICLR 2025Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing
42. ICLR 2025Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
43. ICLR 2025DUALFormer: Dual Graph Transformer
44. ICLR 2025Edge Prompt Tuning for Graph Neural Networks
45. ICLR 2025Energy-based Backdoor Defense Against Federated Graph Learning
46. ICLR 2025Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
47. ICLR 2025Factor Graph-based Interpretable Neural Networks
48. ICLR 2025Generalizing Weisfeiler-Lehman Kernels to Subgraphs
49. ICLR 2025Graph Assisted Offline-Online Deep Reinforcement Learning for Dynamic Workflow Scheduling
50. ICLR 2025Graph Neural Networks Can (Often) Count Substructures

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.