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 728 篇，第 2 / 15 页

51. ICLR 2023Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
52. ICLR 2023Graph Neural Networks for Link Prediction with Subgraph Sketching
53. ICLR 2023Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
54. ICLR 2023GReTo: Remedying dynamic graph topology-task discordance via target homophily
55. ICLR 2023Grounding Graph Network Simulators using Physical Sensor Observations
56. ICLR 2023Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
57. ICLR 2023Imitating Graph-Based Planning with Goal-Conditioned Policies
58. ICLR 2023Interpretable Geometric Deep Learning via Learnable Randomness Injection
59. ICLR 2023Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
60. ICLR 2023Latent Graph Inference using Product Manifolds
61. ICLR 2023Learnable Graph Convolutional Attention Networks
62. ICLR 2023Learnable Topological Features For Phylogenetic Inference via Graph Neural Networks
63. ICLR 2023Learning Fair Graph Representations via Automated Data Augmentations
64. ICLR 2023Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network
65. ICLR 2023Learning Hierarchical Protein Representations via Complete 3D Graph Networks
66. ICLR 2023Learning rigid dynamics with face interaction graph networks
67. ICLR 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
68. ICLR 2023Limitless Stability for Graph Convolutional Networks
69. ICLR 2023Link Prediction with Non-Contrastive Learning
70. ICLR 2023LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence
71. ICLR 2023Logical Message Passing Networks with One-hop Inference on Atomic Formulas
72. ICLR 2023MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning
73. ICLR 2023Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules
74. ICLR 2023NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
75. ICLR 2023On Compositional Uncertainty Quantification for Seq2seq Graph Parsing
76. ICLR 2023On Representing Linear Programs by Graph Neural Networks
77. ICLR 2023On Representing Mixed-Integer Linear Programs by Graph Neural Networks
78. ICLR 2023Rethinking Graph Lottery Tickets: Graph Sparsity Matters
79. ICLR 2023Rethinking the Expressive Power of GNNs via Graph Biconnectivity
80. ICLR 2023REVISITING PRUNING AT INITIALIZATION THROUGH THE LENS OF RAMANUJAN GRAPH
81. ICLR 2023Revisiting Robustness in Graph Machine Learning
82. ICLR 2023Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
83. ICLR 2023Robust Graph Dictionary Learning
84. ICLR 2023Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective
85. ICLR 2023Serving Graph Compression for Graph Neural Networks
86. ICLR 2023Specformer: Spectral Graph Neural Networks Meet Transformers
87. ICLR 2023Strategic Classification with Graph Neural Networks
88. ICLR 2023Subsampling in Large Graphs Using Ricci Curvature
89. ICLR 2023Towards Open Temporal Graph Neural Networks
90. ICLR 2023Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States
91. ICLR 2023Unveiling the sampling density in non-uniform geometric graphs
92. ICML 2023A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
93. ICML 2023A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
94. ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
95. ICML 2023A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
96. ICML 2023Adaptive Smoothing Gradient Learning for Spiking Neural Networks
97. ICML 2023Additive Causal Bandits with Unknown Graph
98. ICML 2023All in a Row: Compressed Convolution Networks for Graphs
99. ICML 2023Alternately Optimized Graph Neural Networks
100. ICML 2023Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels

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。