深度学习 · graph neural networks论文趋势 2023–2026

主要结论

• 深度学习 · graph neural networks论文从 2023 年的 728 篇降到 2026 年的 326 篇，热度在回落。
• NeurIPS 是发深度学习 · graph neural networks论文最多的会议，2023–2026 年共 842 篇，占四个会议总量的 51%。
• 交叉方向里涨得最快的是数据集与基准：同时属于两个主题的论文从 2023 年的 9 篇增至 2026 年的 20 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 5；同组增长最快的扩散模型是 4.1 倍，深度学习 · graph neural networks是 0.4 倍。

代表论文

按年份和会议均匀挑选，方便快速看到每一届的样貌

• 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

全部深度学习 · graph neural networks论文

共 68 篇，第 1 / 2 页

1. ICLR 2023$\mathscr{N}$-WL: A New Hierarchy of Expressivity for Graph Neural Networks
2. ICLR 2023$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks
3. ICLR 2023A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
4. ICLR 2023A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps
5. ICLR 2023A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks
6. ICLR 2023Agent-based Graph Neural Networks
7. ICLR 2023Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
8. ICLR 2023Are More Layers Beneficial to Graph Transformers?
9. ICLR 2023AutoGT: Automated Graph Transformer Architecture Search
10. ICLR 2023Automated Data Augmentations for Graph Classification
11. ICLR 2023AutoTransfer: AutoML with Knowledge Transfer - An Application to Graph Neural Networks
12. ICLR 2023Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs
13. ICLR 2023Characterizing the Influence of Graph Elements
14. ICLR 2023Cycle to Clique (Cy2C) Graph Neural Network: A Sight to See beyond Neighborhood Aggregation
15. ICLR 2023DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks
16. ICLR 2023DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion
17. ICLR 2023Do We Really Need Complicated Model Architectures For Temporal Networks?
18. ICLR 2023Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
19. ICLR 2023Effects of Graph Convolutions in Multi-layer Networks
20. ICLR 2023Efficient Model Updates for Approximate Unlearning of Graph-Structured Data
21. ICLR 2023Energy-based Out-of-Distribution Detection for Graph Neural Networks
22. ICLR 2023Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
23. ICLR 2023ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion
24. ICLR 2023Fair Attribute Completion on Graph with Missing Attributes
25. ICLR 2023FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
26. ICLR 2023Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation
27. ICLR 2023Graph Contrastive Learning for Skeleton-based Action Recognition
28. ICLR 2023Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
29. ICLR 2023Graph Neural Networks for Link Prediction with Subgraph Sketching
30. ICLR 2023Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
31. ICLR 2023GReTo: Remedying dynamic graph topology-task discordance via target homophily
32. ICLR 2023Grounding Graph Network Simulators using Physical Sensor Observations
33. ICLR 2023Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
34. ICLR 2023Imitating Graph-Based Planning with Goal-Conditioned Policies
35. ICLR 2023Interpretable Geometric Deep Learning via Learnable Randomness Injection
36. ICLR 2023Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
37. ICLR 2023Latent Graph Inference using Product Manifolds
38. ICLR 2023Learnable Graph Convolutional Attention Networks
39. ICLR 2023Learnable Topological Features For Phylogenetic Inference via Graph Neural Networks
40. ICLR 2023Learning Fair Graph Representations via Automated Data Augmentations
41. ICLR 2023Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network
42. ICLR 2023Learning Hierarchical Protein Representations via Complete 3D Graph Networks
43. ICLR 2023Learning rigid dynamics with face interaction graph networks
44. ICLR 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
45. ICLR 2023Limitless Stability for Graph Convolutional Networks
46. ICLR 2023Link Prediction with Non-Contrastive Learning
47. ICLR 2023LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence
48. ICLR 2023Logical Message Passing Networks with One-hop Inference on Atomic Formulas
49. ICLR 2023MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning
50. ICLR 2023Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · graph neural networks论文？

共 1,648 篇，按年份分别是 2023 年 728 篇、2024 年 297 篇、2025 年 297 篇、2026 年 326 篇。

NeurIPS 2026 有多少篇深度学习 · graph neural networks论文？

NeurIPS 2026 有 102 篇深度学习 · graph neural networks论文，上一届是 91 篇。

ICLR 2026 有多少篇深度学习 · graph neural networks论文？

ICLR 2026 有 32 篇深度学习 · graph neural networks论文，上一届是 50 篇。

ICML 2026 有多少篇深度学习 · graph neural networks论文？

ICML 2026 有 168 篇深度学习 · graph neural networks论文，上一届是 141 篇。

CVPR 2026 有多少篇深度学习 · graph neural networks论文？

CVPR 2026 有 24 篇深度学习 · graph neural networks论文，上一届是 15 篇。

哪个会议的深度学习 · graph neural networks论文最多？

NeurIPS，2023–2026 年共 842 篇。