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

91 papers, page 1 of 2

1. NeurIPS 20253D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding
2. NeurIPS 2025A Little Depth Goes a Long Way: The Expressive Power of Log-Depth Transformers
3. NeurIPS 2025Adjusted Count Quantification Learning on Graphs
4. NeurIPS 2025An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph
5. NeurIPS 2025Association-Focused Path Aggregation for Graph Fraud Detection
6. NeurIPS 2025Coloring Learning for Heterophilic Graph Representation
7. NeurIPS 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework
8. NeurIPS 2025DCA: Graph-Guided Deep Embedding Clustering for Brain Atlases
9. NeurIPS 2025Depth-Width Tradeoffs for Transformers on Graph Tasks
10. NeurIPS 2025DGCBench: A Deep Graph Clustering Benchmark
11. NeurIPS 2025DiffLiG: Diffusion-enhanced Liquid Graph with Attention Propagation for Grid-to-Station Precipitation Correction
12. NeurIPS 2025Diffusion-Guided Graph Data Augmentation
13. NeurIPS 2025Distribution Learning Meets Graph Structure Sampling
14. NeurIPS 2025DualEqui: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules
15. NeurIPS 2025Dynamic Configuration for Cutting Plane Separators via Reinforcement Learning on Incremental Graph
16. NeurIPS 2025Effects of Dropout on Performance in Long-range Graph Learning Tasks
17. NeurIPS 2025Enforcing convex constraints in Graph Neural Networks
18. NeurIPS 2025Enhancing Graph Classification Robustness with Singular Pooling
19. NeurIPS 2025EventMG: Efficient Multilevel Mamba-Graph Learning for Spatiotemporal Event Representation
20. NeurIPS 2025EverybodyDance: Bipartite Graph–Based Identity Correspondence for Multi-Character Animation
21. NeurIPS 2025FedIGL: Federated Invariant Graph Learning for Non-IID Graphs
22. NeurIPS 2025FlowPrune: Accelerating Attention Flow Calculation by Pruning Flow Network
23. NeurIPS 2025FoGE: Fock Space inspired encoding for graph prompting
24. NeurIPS 2025GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights
25. NeurIPS 2025GD$^2$: Robust Graph Learning under Label Noise via Dual-View Prediction Discrepancy
26. NeurIPS 2025Generative Graph Pattern Machine
27. NeurIPS 2025Geometry-Aware Edge Pooling for Graph Neural Networks
28. NeurIPS 2025Graph Alignment via Birkhoff Relaxation
29. NeurIPS 2025Graph Persistence goes Spectral
30. NeurIPS 2025Graph Your Own Prompt
31. NeurIPS 2025GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining
32. NeurIPS 2025GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data
33. NeurIPS 2025GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
34. NeurIPS 2025Hierarchical Shortest-Path Graph Kernel Network
35. NeurIPS 2025Higher-Order Learning with Graph Neural Networks via Hypergraph Encodings
36. NeurIPS 2025How Does Topology Bias Distort Message Passing in Graph Recommender? A Dirichlet Energy Perspective
37. NeurIPS 2025HubGT: Fast Graph Transformer with Decoupled Hierarchy Labeling
38. NeurIPS 2025HYPERION: Fine-Grained Hypersphere Alignment for Robust Federated Graph Learning
39. NeurIPS 2025HyperMixup: Hypergraph-Augmented with Higher-order Information Mixup
40. NeurIPS 2025Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
41. NeurIPS 2025IPSI: Enhancing Structural Inference with Automatically Learned Structural Priors
42. NeurIPS 2025Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models
43. NeurIPS 2025LBMKGC: Large Model-Driven Balanced Multimodal Knowledge Graph Completion
44. NeurIPS 2025Learning Repetition-Invariant Representations for Polymer Informatics
45. NeurIPS 2025Local-Global Coupling Spiking Graph Transformer for Brain Disorders Diagnosis from Two Perspectives
46. NeurIPS 2025Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization
47. NeurIPS 2025Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness–Generalization Perspective
48. NeurIPS 2025MaNGO — Adaptable Graph Network Simulators via Meta-Learning
49. NeurIPS 2025Memorization in Graph Neural Networks
50. NeurIPS 2025Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow Variants

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.