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

ICML/NeurIPS 深度学习方向：graph_neural_networks

统计范围：NeurIPS、ICLR、ICML、CVPR 四个会议，2023–2026 年，共 1,648 篇被归入该主题的论文。

主要结论

每年论文数

各会议论文数

按年份和会议统计

深度学习 · graph neural networks论文数，按年份和会议
年份NeurIPSICLRICMLCVPR合计
20234896814823728
2024160517610297
2025915014115297
20261023216824326

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

全部深度学习 · graph neural networks论文

年份全部2023202420252026
会议全部NeurIPSICLRICMLCVPR

共 842 篇，第 10 / 17 页

  1. NeurIPS 2023D4Explainer: In-distribution Explanations of Graph Neural Network via Discrete Denoising Diffusion
  2. NeurIPS 2023Data-Centric Learning from Unlabeled Graphs with Diffusion Model
  3. NeurIPS 2023Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
  4. NeurIPS 2023Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
  5. NeurIPS 2023Deep Insights into Noisy Pseudo Labeling on Graph Data
  6. NeurIPS 2023Deep learning with kernels through RKHM and the Perron-Frobenius operator
  7. NeurIPS 2023Deep Patch Visual Odometry
  8. NeurIPS 2023Deep Recurrent Optimal Stopping
  9. NeurIPS 2023Deep Stochastic Processes via Functional Markov Transition Operators
  10. NeurIPS 2023DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
  11. NeurIPS 2023DeepPCR: Parallelizing Sequential Operations in Neural Networks
  12. NeurIPS 2023Degraded Polygons Raise Fundamental Questions of Neural Network Perception
  13. NeurIPS 2023Demystifying Oversmoothing in Attention-Based Graph Neural Networks
  14. NeurIPS 2023Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
  15. NeurIPS 2023Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
  16. NeurIPS 2023Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes
  17. NeurIPS 2023Differentiable sorting for censored time-to-event data.
  18. NeurIPS 2023Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection
  19. NeurIPS 2023Diffused Redundancy in Pre-trained Representations
  20. NeurIPS 2023Diffusion Model for Graph Inverse Problems: Towards Effective Source Localization on Complex Networks
  21. NeurIPS 2023Diffusion Probabilistic Models for Structured Node Classification
  22. NeurIPS 2023Directional diffusion models for graph representation learning
  23. NeurIPS 2023Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
  24. NeurIPS 2023Distributed Personalized Empirical Risk Minimization
  25. NeurIPS 2023Distributionally Robust Skeleton Learning of Discrete Bayesian Networks
  26. NeurIPS 2023DiViNeT: 3D Reconstruction from Disparate Views using Neural Template Regularization
  27. NeurIPS 2023Does a sparse ReLU network training problem always admit an optimum ?
  28. NeurIPS 2023Does Graph Distillation See Like Vision Dataset Counterpart?
  29. NeurIPS 2023Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
  30. NeurIPS 2023Don’t just prune by magnitude! Your mask topology is a secret weapon
  31. NeurIPS 2023Double Gumbel Q-Learning
  32. NeurIPS 2023DSR: Dynamical Surface Representation as Implicit Neural Networks for Protein
  33. NeurIPS 2023Dynamic Sparsity Is Channel-Level Sparsity Learner
  34. NeurIPS 2023Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes
  35. NeurIPS 2023Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
  36. NeurIPS 2023Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks
  37. NeurIPS 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
  38. NeurIPS 2023Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
  39. NeurIPS 2023Efficient Beam Tree Recursion
  40. NeurIPS 2023Efficient Learning of Linear Graph Neural Networks via Node Subsampling
  41. NeurIPS 2023Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization
  42. NeurIPS 2023Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
  43. NeurIPS 2023Efficiently incorporating quintuple interactions into geometric deep learning force fields
  44. NeurIPS 2023EICIL: Joint Excitatory Inhibitory Cycle Iteration Learning for Deep Spiking Neural Networks
  45. NeurIPS 2023Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity
  46. NeurIPS 2023Empowering Convolutional Neural Nets with MetaSin Activation
  47. NeurIPS 2023Encoding Human Behavior in Information Design through Deep Learning
  48. NeurIPS 2023Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
  49. NeurIPS 2023Enhancing Sharpness-Aware Optimization Through Variance Suppression
  50. NeurIPS 2023Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift

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用 paperlist 查深度学习 · graph neural networks在 NeurIPS 2023–2026 的论文趋势，哪一年涨得最快？

AI 会调用的工具：get_topic_stats {"topic": "dl-graph-neural-networks"}

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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。

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