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 91 篇，第 2 / 2 页

51. NeurIPS 2025Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
52. NeurIPS 2025Network two-sample test for block models
53. NeurIPS 2025NeuroH-TGL: Neuro-Heterogeneity Guided Temporal Graph Learning Strategy for Brain Disease Diagnosis
54. NeurIPS 2025Object-Centric Representation Learning for Enhanced 3D Semantic Scene Graph Prediction
55. NeurIPS 2025On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
56. NeurIPS 2025On topological descriptors for graph products
57. NeurIPS 2025One for All: Universal Topological Primitive Transfer for Graph Structure Learning
58. NeurIPS 2025Optimal Graph Clustering without Edge Density Signals
59. NeurIPS 2025Over-squashing in Spatiotemporal Graph Neural Networks
60. NeurIPS 2025Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning
61. NeurIPS 2025ProfiX: Improving Profile-Guided Optimization in Compilers with Graph Neural Networks
62. NeurIPS 2025Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization
63. NeurIPS 2025Pseudo-Riemannian Graph Transformer
64. NeurIPS 2025Refining Norms: A Post-hoc Framework for OOD Detection in Graph Neural Networks
65. NeurIPS 2025Relieving the Over-Aggregating Effect in Graph Transformers
66. NeurIPS 2025Rethinking Tokenized Graph Transformers for Node Classification
67. NeurIPS 2025Revisiting 1-peer exponential graph for enhancing decentralized learning efficiency
68. NeurIPS 2025Revolutionizing Graph Aggregation: From Suppression to Amplification via BoostGCN
69. NeurIPS 2025Robust Graph Condensation via Classification Complexity Mitigation
70. NeurIPS 2025Self-Assembling Graph Perceptrons
71. NeurIPS 2025Semi-supervised Graph Anomaly Detection via Robust Homophily Learning
72. NeurIPS 2025SignFlow Bipartite Subgraph Network For Large-Scale Graph Link Sign Prediction
73. NeurIPS 2025Sketch-Augmented Features Improve Learning Long-Range Dependencies in Graph Neural Networks
74. NeurIPS 2025SONAR: Long-Range Graph Propagation Through Information Waves
75. NeurIPS 2025Sound Logical Explanations for Mean Aggregation Graph Neural Networks
76. NeurIPS 2025Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum
77. NeurIPS 2025Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
78. NeurIPS 2025TAMI: Taming Heterogeneity in Temporal Interactions for Temporal Graph Link Prediction
79. NeurIPS 2025Taxonomy of reduction matrices for Graph Coarsening
80. NeurIPS 2025The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics
81. NeurIPS 2025The quest for the GRAph Level autoEncoder (GRALE)
82. NeurIPS 2025TopER: Topological Embeddings in Graph Representation Learning
83. NeurIPS 2025Topology-aware Graph Diffusion Model with Persistent Homology
84. NeurIPS 2025Topology-Aware Learning of Tubular Manifolds via SE(3)-Equivariant Network on Ball B-Spline Curve
85. NeurIPS 2025Towards Generalizable Retina Vessel Segmentation with Deformable Graph Priors
86. NeurIPS 2025Towards Pre-trained Graph Condensation via Optimal Transport
87. NeurIPS 2025Uncertain Knowledge Graph Completion via Semi-Supervised Confidence Distribution Learning
88. NeurIPS 2025Universally Invariant Learning in Equivariant GNNs
89. NeurIPS 2025Unsupervised Federated Graph Learning
90. NeurIPS 2025VGGT-SLAM: Dense RGB SLAM Optimized on the SL(4) Manifold
91. NeurIPS 2025You Only Spectralize Once: Taking a Spectral Detour to Accelerate Graph Neural Network

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。