图神经网络论文趋势 2023–2026
GNN 架构
统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 1,038 篇被归入该主题的论文。
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。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 251 | 69 | 96 | 18 | 434 |
| 2024 | 89 | 45 | 68 | 6 | 208 |
| 2025 | 75 | 45 | 81 | 12 | 213 |
| 2026 | 74 | 28 | 68 | 13 | 183 |
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 313 篇，第 6 / 7 页
- ICML 2023From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
- ICML 2023Function-Space Regularization in Neural Networks: A Probabilistic Perspective
- ICML 2023GC-Flow: A Graph-Based Flow Network for Effective Clustering
- ICML 2023Generalizing Neural Wave Functions
- ICML 2023Generated Graph Detection
- ICML 2023Generative Graph Dictionary Learning
- ICML 2023Geometric Clifford Algebra Networks
- ICML 2023GFlowOut: Dropout with Generative Flow Networks
- ICML 2023GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming
- ICML 2023Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks
- ICML 2023Graph Generative Model for Benchmarking Graph Neural Networks
- ICML 2023Graph Inductive Biases in Transformers without Message Passing
- ICML 2023Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
- ICML 2023Graph Mixup with Soft Alignments
- ICML 2023Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
- ICML 2023Graph Neural Networks with Learnable and Optimal Polynomial Bases
- ICML 2023Graph Neural Tangent Kernel: Convergence on Large Graphs
- ICML 2023Graph Positional Encoding via Random Feature Propagation
- ICML 2023Graph Switching Dynamical Systems
- ICML 2023GREAD: Graph Neural Reaction-Diffusion Networks
- ICML 2023Half-Hop: A graph upsampling approach for slowing down message passing
- ICML 2023HOPE: High-order Graph ODE For Modeling Interacting Dynamics
- ICML 2023Implicit Graph Neural Networks: A Monotone Operator Viewpoint
- ICML 2023Improving Graph Neural Networks with Learnable Propagation Operators
- ICML 2023InGram: Inductive Knowledge Graph Embedding via Relation Graphs
- ICML 2023LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
- ICML 2023Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
- ICML 2023LinSATNet: The Positive Linear Satisfiability Neural Networks
- ICML 2023Local Vertex Colouring Graph Neural Networks
- ICML 2023Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
- ICML 2023MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
- ICML 2023Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
- ICML 2023Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
- ICML 2023Moccasin: Efficient Tensor Rematerialization for Neural Networks
- ICML 2023Multi-class Graph Clustering via Approximated Effective $p$-Resistance
- ICML 2023Neural networks trained with SGD learn distributions of increasing complexity
- ICML 2023Neural signature kernels as infinite-width-depth-limits of controlled ResNets
- ICML 2023Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks
- ICML 2023Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
- ICML 2023On the Expressive Power of Geometric Graph Neural Networks
- ICML 2023On the Initialization of Graph Neural Networks
- ICML 2023Path Neural Networks: Expressive and Accurate Graph Neural Networks
- ICML 2023Perturbation Analysis of Neural Collapse
- ICML 2023Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
- ICML 2023Randomized Schur Complement Views for Graph Contrastive Learning
- ICML 2023Relevant Walk Search for Explaining Graph Neural Networks
- ICML 2023Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
- ICML 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networks
- ICML 2023RGE: A Repulsive Graph Rectification for Node Classification via Influence
- ICML 2023RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
用 paperlist 查图神经网络在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？
AI 会调用的工具：get_topic_stats {"topic": "graph-neural-networks-topic"}
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。