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 1,038 篇，第 21 / 21 页

1001. NeurIPS 2023Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
1002. NeurIPS 2023Stochastic Approximation Algorithms for Systems of Interacting Particles
1003. NeurIPS 2023Structure of universal formulas
1004. NeurIPS 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
1005. NeurIPS 2023SyncTREE: Fast Timing Analysis for Integrated Circuit Design through a Physics-informed Tree-based Graph Neural Network
1006. NeurIPS 2023Tailoring Self-Attention for Graph via Rooted Subtrees
1007. NeurIPS 2023Taming Local Effects in Graph-based Spatiotemporal Forecasting
1008. NeurIPS 2023TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
1009. NeurIPS 2023The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
1010. NeurIPS 2023The expressive power of pooling in Graph Neural Networks
1011. NeurIPS 2023The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models
1012. NeurIPS 2023The Separation Capacity of Random Neural Networks
1013. NeurIPS 2023Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
1014. NeurIPS 2023Three Iterations of (d − 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points
1015. NeurIPS 2023Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
1016. NeurIPS 2023Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach
1017. NeurIPS 2023Towards Label Position Bias in Graph Neural Networks
1018. NeurIPS 2023Towards Self-Interpretable Graph-Level Anomaly Detection
1019. NeurIPS 2023Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks
1020. NeurIPS 2023Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies
1021. NeurIPS 2023Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
1022. NeurIPS 2023Training Neural Networks is NP-Hard in Fixed Dimension
1023. NeurIPS 2023Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?
1024. NeurIPS 2023Transformers over Directed Acyclic Graphs
1025. NeurIPS 2023Trial matching: capturing variability with data-constrained spiking neural networks
1026. NeurIPS 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
1027. NeurIPS 2023Universal Prompt Tuning for Graph Neural Networks
1028. NeurIPS 2023Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
1029. NeurIPS 2023Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision
1030. NeurIPS 2023Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation
1031. NeurIPS 2023V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs
1032. NeurIPS 2023Variational Annealing on Graphs for Combinatorial Optimization
1033. NeurIPS 2023Wasserstein distributional robustness of neural networks
1034. NeurIPS 2023What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
1035. NeurIPS 2023What Planning Problems Can A Relational Neural Network Solve?
1036. NeurIPS 2023When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability
1037. NeurIPS 2023Window-Based Distribution Shift Detection for Deep Neural Networks
1038. NeurIPS 2023Zero-One Laws of Graph Neural 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。