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 69 篇，第 1 / 2 页

1. ICLR 2023$\mathcal{O}$-GNN: incorporating ring priors into molecular modeling
2. ICLR 2023$\mathscr{N}$-WL: A New Hierarchy of Expressivity for Graph Neural Networks
3. ICLR 2023A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
4. ICLR 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?
5. ICLR 2023A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps
6. ICLR 2023A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks
7. ICLR 2023ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks
8. ICLR 2023Agent-based Graph Neural Networks
9. ICLR 2023Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel
10. ICLR 2023Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
11. ICLR 2023Are More Layers Beneficial to Graph Transformers?
12. ICLR 2023AutoGT: Automated Graph Transformer Architecture Search
13. ICLR 2023Automated Data Augmentations for Graph Classification
14. ICLR 2023AutoTransfer: AutoML with Knowledge Transfer - An Application to Graph Neural Networks
15. ICLR 2023Backpropagation through Combinatorial Algorithms: Identity with Projection Works
16. ICLR 2023Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs
17. ICLR 2023Characterizing the Influence of Graph Elements
18. ICLR 2023CktGNN: Circuit Graph Neural Network for Electronic Design Automation
19. ICLR 2023Cycle to Clique (Cy2C) Graph Neural Network: A Sight to See beyond Neighborhood Aggregation
20. ICLR 2023DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks
21. ICLR 2023Do We Really Need Complicated Model Architectures For Temporal Networks?
22. ICLR 2023EAGLE: Large-scale Learning of Turbulent Fluid Dynamics with Mesh Transformers
23. ICLR 2023Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
24. ICLR 2023Effects of Graph Convolutions in Multi-layer Networks
25. ICLR 2023Efficient Model Updates for Approximate Unlearning of Graph-Structured Data
26. ICLR 2023Empowering Graph Representation Learning with Test-Time Graph Transformation
27. ICLR 2023Energy-based Out-of-Distribution Detection for Graph Neural Networks
28. ICLR 2023Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
29. ICLR 2023Explaining Temporal Graph Models through an Explorer-Navigator Framework
30. ICLR 2023ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion
31. ICLR 2023Fair Attribute Completion on Graph with Missing Attributes
32. ICLR 2023FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
33. ICLR 2023Fundamental Limits in Formal Verification of Message-Passing Neural Networks
34. ICLR 2023GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
35. ICLR 2023GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks
36. ICLR 2023Graph Domain Adaptation via Theory-Grounded Spectral Regularization
37. ICLR 2023Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
38. ICLR 2023Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs
39. ICLR 2023Graph Neural Networks for Link Prediction with Subgraph Sketching
40. ICLR 2023GReTo: Remedying dynamic graph topology-task discordance via target homophily
41. ICLR 2023Grounding Graph Network Simulators using Physical Sensor Observations
42. ICLR 2023Latent Graph Inference using Product Manifolds
43. ICLR 2023Learnable Graph Convolutional Attention Networks
44. ICLR 2023Learnable Topological Features For Phylogenetic Inference via Graph Neural Networks
45. ICLR 2023Learning Fair Graph Representations via Automated Data Augmentations
46. ICLR 2023Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network
47. ICLR 2023Learning Hierarchical Protein Representations via Complete 3D Graph Networks
48. ICLR 2023Learning rigid dynamics with face interaction graph networks
49. ICLR 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
50. ICLR 2023Limitless Stability for Graph Convolutional Networks

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。