图神经网络论文趋势 2023–2026

主要结论

• 图神经网络论文从 2023 年的 434 篇降到 2026 年的 183 篇，热度在回落。
• NeurIPS 是发图神经网络论文最多的会议，2023–2026 年共 489 篇，占四个会议总量的 47%。
• 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 10 篇增至 2026 年的 10 篇。
• 在「图与结构化数据」的 5 个主题里，它的 2026 年论文数排第 4；同组增长最快的时间序列是 3.2 倍，图神经网络是 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 Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry
• ICML 2026Aitchison Embeddings for Learning Compositional Graph Representations
• ICLR 2026Certified Evaluation of Model-Level Explanations for Graph Neural Networks
• CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
• NeurIPS 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework
• ICML 2025Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts
• ICLR 2025Edge Prompt Tuning for Graph Neural Networks
• CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
• NeurIPS 2024[Re] Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework"
• ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
• ICLR 2024Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
• CVPR 2024FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer
• NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
• ICML 2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
• ICLR 2023$\mathscr{N}$-WL: A New Hierarchy of Expressivity for Graph Neural Networks
• CVPR 2023Adversarially Robust Neural Architecture Search for Graph Neural Networks
• NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
• ICML 2026Any-dimensional invariant universality
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Global-Aware Edge Prioritization for Pose Graph Initialization

全部图神经网络论文

共 89 篇，第 1 / 2 页

1. NeurIPS 2024[Re] Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework"
2. NeurIPS 2024Almost Surely Asymptotically Constant Graph Neural Networks
3. NeurIPS 2024An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval
4. NeurIPS 2024Analysis of Corrected Graph Convolutions
5. NeurIPS 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learning
6. NeurIPS 2024Are Graph Neural Networks Optimal Approximation Algorithms?
7. NeurIPS 2024Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
8. NeurIPS 2024Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
9. NeurIPS 2024Boosting Graph Pooling with Persistent Homology
10. NeurIPS 2024Can Graph Learning Improve Planning in LLM-based Agents?
11. NeurIPS 2024Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
12. NeurIPS 2024Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
13. NeurIPS 2024Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention
14. NeurIPS 2024Deep Graph Mating
15. NeurIPS 2024Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module
16. NeurIPS 2024Deep Homomorphism Networks
17. NeurIPS 2024DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
18. NeurIPS 2024DiGRAF: Diffeomorphic Graph-Adaptive Activation Function
19. NeurIPS 2024Distributed-Order Fractional Graph Operating Network
20. NeurIPS 2024DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
21. NeurIPS 2024Dynamic Rescaling for Training GNNs
22. NeurIPS 2024EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection
23. NeurIPS 2024Energy-based Epistemic Uncertainty for Graph Neural Networks
24. NeurIPS 2024Enhancing Chess Reinforcement Learning with Graph Representation
25. NeurIPS 2024Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
26. NeurIPS 2024Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
27. NeurIPS 2024Even Sparser Graph Transformers
28. NeurIPS 2024Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks
29. NeurIPS 2024Faster Local Solvers for Graph Diffusion Equations
30. NeurIPS 2024FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features
31. NeurIPS 2024Generalizing CNNs to graphs with learnable neighborhood quantization
32. NeurIPS 2024Geometric Trajectory Diffusion Models
33. NeurIPS 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Models
34. NeurIPS 2024Gradient Rewiring for Editable Graph Neural Network Training
35. NeurIPS 2024GRANOLA: Adaptive Normalization for Graph Neural Networks
36. NeurIPS 2024Graph Coarsening with Message-Passing Guarantees
37. NeurIPS 2024Graph Convolutions Enrich the Self-Attention in Transformers!
38. NeurIPS 2024Graph Neural Networks and Arithmetic Circuits
39. NeurIPS 2024Graph neural networks and non-commuting operators
40. NeurIPS 2024Graph Neural Networks Do Not Always Oversmooth
41. NeurIPS 2024Graph Neural Networks Need Cluster-Normalize-Activate Modules
42. NeurIPS 2024Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
43. NeurIPS 2024Graphcode: Learning from multiparameter persistent homology using graph neural networks
44. NeurIPS 2024GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction
45. NeurIPS 2024GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
46. NeurIPS 2024GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules
47. NeurIPS 2024HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
48. NeurIPS 2024HGDL: Heterogeneous Graph Label Distribution Learning
49. NeurIPS 2024If You Want to Be Robust, Be Wary of Initialization
50. NeurIPS 2024Improving Generalization of Dynamic Graph Learning via Environment Prompt

常见问题

2023–2026 年四大会议一共有多少篇图神经网络论文？

共 1,038 篇，按年份分别是 2023 年 434 篇、2024 年 208 篇、2025 年 213 篇、2026 年 183 篇。

NeurIPS 2026 有多少篇图神经网络论文？

NeurIPS 2026 有 74 篇图神经网络论文，上一届是 75 篇。

ICLR 2026 有多少篇图神经网络论文？

ICLR 2026 有 28 篇图神经网络论文，上一届是 45 篇。

ICML 2026 有多少篇图神经网络论文？

ICML 2026 有 68 篇图神经网络论文，上一届是 81 篇。

CVPR 2026 有多少篇图神经网络论文？

CVPR 2026 有 13 篇图神经网络论文，上一届是 12 篇。

哪个会议的图神经网络论文最多？

NeurIPS，2023–2026 年共 489 篇。