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 102 篇，第 2 / 3 页

51. NeurIPS 2026HiddenPathQA: A Benchmark for Knowledge Graph Question Answering When Questions Hide Their Paths
52. NeurIPS 2026Hierarchical Graph Representation Learning with Pooling-Induced Substructures
53. NeurIPS 2026Hierarchical Regime-Conditioned Dynamics for Spatiotemporal Graphs
54. NeurIPS 2026HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis
55. NeurIPS 2026HoTS: Homophily-Aware Temperature Scaling for Graph Neural Network Calibration
56. NeurIPS 2026IncAgg: Efficient Memory-Enhanced Graph Learning via Incremental Aggregation
57. NeurIPS 2026Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists
58. NeurIPS 2026Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling
59. NeurIPS 2026Invariant Hyperbolic Unfolding: Radial Canonicalization for Label-Free Cross-Graph Link Prediction
60. NeurIPS 2026Learning Minimal Sufficient Evidence Graphs for GraphRAG via Nash-Guided Optimization
61. NeurIPS 2026Learning Pseudo-Riemannian Manifolds for Heterophilic Graphs via Graph Signature
62. NeurIPS 2026Long-Range Spatio-Temporal Graph Propagation Through Oscillations
63. NeurIPS 2026Matrix-Free Stochastic Training of Low-Rank Spectral Graph Learning via Randomized Adaptive Spectral Estimation
64. NeurIPS 2026MCGI: Manifold-Consistent Graph Indexing for Billion-Scale Disk-Resident Vector Search
65. NeurIPS 2026MicroWorld: Empowering Multimodal Large Language Models to Bridge the Microscopic Domain Gap with Multimodal Attribute Graph
66. NeurIPS 2026MIRAGE: Hierarchical MI-Surrogate Regulation for Graph Contrastive Learning
67. NeurIPS 2026MixScentNet: A Multiscale Graph-based Framework for Predicting Scent Mixture Perception
68. NeurIPS 2026Module-Aware Optimization for Graph Neural Networks
69. NeurIPS 2026Multilateral Resistance-Guided Graph Message Passing for Trade Flow Prediction
70. NeurIPS 2026Neighbor-Aware Snapshot-Based Temporal Graph Learning
71. NeurIPS 2026Neutral-atom quantum features as complementary structural encodings for graph learning
72. NeurIPS 2026Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
73. NeurIPS 2026OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
74. NeurIPS 2026On Worst-Case Guarantees for Graph-Based Nearest-Neighbor Search
75. NeurIPS 2026One-Shot Federated Graph Learning via High-Fidelity Proxies and Transferability-Guided Collaboration
76. NeurIPS 2026Orlicz–Sobolev with Musielak: An Efficient Regularization Approach for Graph-based IPM
77. NeurIPS 2026Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion
78. NeurIPS 2026PANDA: Prior-guided Attentional Dual-path Architecture
79. NeurIPS 2026PATH: A Dual Perspective for High-quality Text-attributed Graph Learning
80. NeurIPS 2026Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
81. NeurIPS 2026Pointwise Lipschitz Continuous Graph Algorithms
82. NeurIPS 2026Practical Non-Stationary Graph Gaussian Processes
83. NeurIPS 2026R-GRec: Relation-Guided Generative Recommendation via Collaborative Graph Supervision
84. NeurIPS 2026Relation-Aware Graph Foundation Model
85. NeurIPS 2026Releasing Anchors from Cross-View Correspondence: Probabilistic Multi-View Anchor Graph Clustering
86. NeurIPS 2026Return to Basics: Very Simple Graph Contrastive Learning via Noise Cancellation Principle
87. NeurIPS 2026Robust Graph Diffusion Model
88. NeurIPS 2026Robust Surrogate Modeling for Explainable Graph Neural Networks
89. NeurIPS 2026S&P: Towards Scalable and Powerful Graph Learning with Hierarchical Structural Acquisition
90. NeurIPS 2026Self-Tuning Graph Filters via State-Dependent Operator Composition
91. NeurIPS 2026Semantically Complementary Spectral Views Learning for Graph-Level Anomaly Detection
92. NeurIPS 2026SGNNBench: A Holistic Evaluation of Spiking Graph Neural Networks on Large-scale Graphs
93. NeurIPS 2026Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
94. NeurIPS 2026Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting
95. NeurIPS 2026SSDGExplainer: Structure-Semantic Dual-Guided Explainer for Graph Neural Networks
96. NeurIPS 2026Towards Universal Black-box Attacks on Graph Neural Networks
97. NeurIPS 2026UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning
98. NeurIPS 2026Uncertainty-Aware Fuzzy Graph Contrastive Learning
99. NeurIPS 2026WavCIL: Wavelet Coefficient-Domain Invariant Learning for Dynamic Graph OOD Generalization
100. NeurIPS 2026Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them

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。