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 28 篇，第 1 / 1 页

1. ICLR 2026A Graph Meta-Network for Learning on Kolmogorov–Arnold Networks
2. ICLR 2026AdS-GNN - a Conformally Equivariant Graph Neural Network
3. ICLR 2026Any-Subgroup Equivariant Networks via Symmetry Breaking
4. ICLR 2026Are we measuring oversmoothing in graph neural networks correctly?
5. ICLR 2026Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation
6. ICLR 2026Certified Evaluation of Model-Level Explanations for Graph Neural Networks
7. ICLR 2026DGNet: Learning Spatiotemporal PDEs with Discrete Green Networks
8. ICLR 2026Dynamic Kernel Graph Sparsifiers
9. ICLR 2026Fair Graph Machine Learning under Adversarial Missingness Processes
10. ICLR 2026Full-Graph vs. Mini-Batch Training: Comprehensive Analysis from a Batch Size and Fan-Out Size Perspective
11. ICLR 2026GGBall: Graph Generative Model on Poincaré Ball
12. ICLR 2026Glance for Context: Learning When to Leverage LLMs for Node-Aware GNN-LLM Fusion
13. ICLR 2026GNN Explanations that do not Explain and How to find Them
14. ICLR 2026GNN-as-Judge: Unleashing the Power of LLMs for Graph Few-shot Semi-supervised Learning with GNN Feedback
15. ICLR 2026Is Graph Unlearning Ready for Practice? A Benchmark on Efficiency, Utility, and Forgetting
16. ICLR 2026Laplacian Kernelized Bandit
17. ICLR 2026Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects
18. ICLR 2026Learning from Historical Activations in Graph Neural Networks
19. ICLR 2026Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrolling
20. ICLR 2026LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
21. ICLR 2026On The Expressive Power of GNN Derivatives
22. ICLR 2026One for Two: A Unified Framework for Imbalanced Graph Classification via Dynamic Balanced Prototype
23. ICLR 2026Out-of-Distribution Graph Models Merging
24. ICLR 2026Paradigm Shift of GNN Explainer from Label Space to Prototypical Representation Space
25. ICLR 2026Relational Graph Transformer
26. ICLR 2026Relatron: Automating Relational Machine Learning over Relational Databases
27. ICLR 2026TopoFormer: Topology Meets Attention for Graph Learning
28. ICLR 2026Weight Space Representation Learning on Diverse NeRF Architectures

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。