深度学习 · graph neural networks论文趋势 2023–2026

主要结论

• 深度学习 · graph neural networks论文从 2023 年的 728 篇降到 2026 年的 326 篇，热度在回落。
• NeurIPS 是发深度学习 · graph neural networks论文最多的会议，2023–2026 年共 842 篇，占四个会议总量的 51%。
• 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 9 篇增至 2026 年的 20 篇。
• 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 5；同组增长最快的扩散模型是 4.1 倍，深度学习 · graph neural networks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 New Perspective on Target-Conditioned Structural Dynamics for Link Prediction in Dynamic Graphs
• ICML 2026A Graph Foundation Model with Cross-Modal Alignment and Modality-Aware Expert Fusion for Multi-Modal Graphs
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
• NeurIPS 2025Depth-Width Tradeoffs for Transformers on Graph Tasks
• ICML 2025A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
• ICLR 2025Factor Graph-based Interpretable Neural Networks
• CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
• ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
• CVPR 2024Learning Structure-from-Motion with Graph Attention Networks
• NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
• ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
• ICLR 2023Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
• CVPR 2023An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity
• NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
• ICML 2026A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
• ICLR 2026GTool: Graph Enhanced Tool Planning with Large Language Model
• CVPR 2026GSNR Graph Smooth Null-Space Representation for Inverse Problems

全部深度学习 · graph neural networks论文

共 1,648 篇，第 21 / 33 页

1001. ICLR 2023Revisiting Robustness in Graph Machine Learning
1002. ICLR 2023Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
1003. ICLR 2023Robust Graph Dictionary Learning
1004. ICLR 2023Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective
1005. ICLR 2023Serving Graph Compression for Graph Neural Networks
1006. ICLR 2023Specformer: Spectral Graph Neural Networks Meet Transformers
1007. ICLR 2023Strategic Classification with Graph Neural Networks
1008. ICLR 2023Subsampling in Large Graphs Using Ricci Curvature
1009. ICLR 2023Towards Open Temporal Graph Neural Networks
1010. ICLR 2023Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States
1011. ICLR 2023Unveiling the sampling density in non-uniform geometric graphs
1012. ICML 2023A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
1013. ICML 2023A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
1014. ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
1015. ICML 2023A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
1016. ICML 2023Adaptive Smoothing Gradient Learning for Spiking Neural Networks
1017. ICML 2023Additive Causal Bandits with Unknown Graph
1018. ICML 2023All in a Row: Compressed Convolution Networks for Graphs
1019. ICML 2023Alternately Optimized Graph Neural Networks
1020. ICML 2023Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels
1021. ICML 2023Bayes-optimal Learning of Deep Random Networks of Extensive-width
1022. ICML 2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
1023. ICML 2023BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning
1024. ICML 2023Boosting Graph Contrastive Learning via Graph Contrastive Saliency
1025. ICML 2023Certified Robust Neural Networks: Generalization and Corruption Resistance
1026. ICML 2023CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling
1027. ICML 2023CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
1028. ICML 2023Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
1029. ICML 2023Conditional Graph Information Bottleneck for Molecular Relational Learning
1030. ICML 2023Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping
1031. ICML 2023Conformal Prediction Sets for Graph Neural Networks
1032. ICML 2023Controllable Neural Symbolic Regression
1033. ICML 2023Deep Graph Representation Learning and Optimization for Influence Maximization
1034. ICML 2023Deep linear networks can benignly overfit when shallow ones do
1035. ICML 2023Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks
1036. ICML 2023Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
1037. ICML 2023Deterministic equivalent and error universality of deep random features learning
1038. ICML 2023Direct Parameterization of Lipschitz-Bounded Deep Networks
1039. ICML 2023Disentangled Multiplex Graph Representation Learning
1040. ICML 2023Distribution Free Prediction Sets for Node Classification
1041. ICML 2023Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
1042. ICML 2023Does a Neural Network Really Encode Symbolic Concepts?
1043. ICML 2023Does Continual Learning Equally Forget All Parameters?
1044. ICML 2023Dropout Reduces Underfitting
1045. ICML 2023Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
1046. ICML 2023ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
1047. ICML 2023Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
1048. ICML 2023Efficient Graph Field Integrators Meet Point Clouds
1049. ICML 2023Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network
1050. ICML 2023Efficient Parametric Approximations of Neural Network Function Space Distance

常见问题

2023–2026 年四大会议一共有多少篇深度学习 · graph neural networks论文？

共 1,648 篇，按年份分别是 2023 年 728 篇、2024 年 297 篇、2025 年 297 篇、2026 年 326 篇。

NeurIPS 2026 有多少篇深度学习 · graph neural networks论文？

NeurIPS 2026 有 102 篇深度学习 · graph neural networks论文，上一届是 91 篇。

ICLR 2026 有多少篇深度学习 · graph neural networks论文？

ICLR 2026 有 32 篇深度学习 · graph neural networks论文，上一届是 50 篇。

ICML 2026 有多少篇深度学习 · graph neural networks论文？

ICML 2026 有 168 篇深度学习 · graph neural networks论文，上一届是 141 篇。

CVPR 2026 有多少篇深度学习 · graph neural networks论文？

CVPR 2026 有 24 篇深度学习 · graph neural networks论文，上一届是 15 篇。

哪个会议的深度学习 · graph neural networks论文最多？

NeurIPS，2023–2026 年共 842 篇。