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 148 篇，第 2 / 3 页

51. ICML 2023GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency
52. ICML 2023Generalizing Neural Wave Functions
53. ICML 2023Generated Graph Detection
54. ICML 2023Generative Graph Dictionary Learning
55. ICML 2023Geometric Clifford Algebra Networks
56. ICML 2023GFlowOut: Dropout with Generative Flow Networks
57. ICML 2023Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks
58. ICML 2023Graph Contrastive Backdoor Attacks
59. ICML 2023Graph Generative Model for Benchmarking Graph Neural Networks
60. ICML 2023Graph Inductive Biases in Transformers without Message Passing
61. ICML 2023Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
62. ICML 2023Graph Mixup with Soft Alignments
63. ICML 2023Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
64. ICML 2023Graph Neural Tangent Kernel: Convergence on Large Graphs
65. ICML 2023Graph Positional Encoding via Random Feature Propagation
66. ICML 2023Graph Switching Dynamical Systems
67. ICML 2023GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
68. ICML 2023GREAD: Graph Neural Reaction-Diffusion Networks
69. ICML 2023Half-Hop: A graph upsampling approach for slowing down message passing
70. ICML 2023Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
71. ICML 2023HOPE: High-order Graph ODE For Modeling Interacting Dynamics
72. ICML 2023How much does Initialization Affect Generalization?
73. ICML 2023Identification of the Adversary from a Single Adversarial Example
74. ICML 2023Implicit Graph Neural Networks: A Monotone Operator Viewpoint
75. ICML 2023Improving Graph Generation by Restricting Graph Bandwidth
76. ICML 2023Improving Graph Neural Networks with Learnable Propagation Operators
77. ICML 2023InGram: Inductive Knowledge Graph Embedding via Relation Graphs
78. ICML 2023LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
79. ICML 2023Learning Neural Constitutive Laws from Motion Observations for Generalizable PDE Dynamics
80. ICML 2023Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments
81. ICML 2023Learning Preconditioners for Conjugate Gradient PDE Solvers
82. ICML 2023Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
83. ICML 2023Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains
84. ICML 2023LinSATNet: The Positive Linear Satisfiability Neural Networks
85. ICML 2023Local Vertex Colouring Graph Neural Networks
86. ICML 2023Maximal Initial Learning Rates in Deep ReLU Networks
87. ICML 2023Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
88. ICML 2023Meta-Learning the Inductive Bias of Simple Neural Circuits
89. ICML 2023MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
90. ICML 2023Minimal Width for Universal Property of Deep RNN
91. ICML 2023Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
92. ICML 2023Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
93. ICML 2023Moccasin: Efficient Tensor Rematerialization for Neural Networks
94. ICML 2023Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
95. ICML 2023MODeL: Memory Optimizations for Deep Learning
96. ICML 2023Multi-class Graph Clustering via Approximated Effective $p$-Resistance
97. ICML 2023Neural networks trained with SGD learn distributions of increasing complexity
98. ICML 2023Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks
99. ICML 2023NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes
100. ICML 2023Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks

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。