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 728 篇，第 14 / 15 页

  1. NeurIPS 2023Sparse Graph Learning from Spatiotemporal Time Series
  2. NeurIPS 2023Spectral Evolution and Invariance in Linear-width Neural Networks
  3. NeurIPS 2023Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
  4. NeurIPS 2023Spike-driven Transformer
  5. NeurIPS 2023Spiking PointNet: Spiking Neural Networks for Point Clouds
  6. NeurIPS 2023Squared Neural Families: A New Class of Tractable Density Models
  7. NeurIPS 2023Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
  8. NeurIPS 2023Statistically Valid Variable Importance Assessment through Conditional Permutations
  9. NeurIPS 2023Stochastic Approximation Algorithms for Systems of Interacting Particles
  10. NeurIPS 2023Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
  11. NeurIPS 2023Structure Learning with Adaptive Random Neighborhood Informed MCMC
  12. NeurIPS 2023Structure of universal formulas
  13. NeurIPS 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
  14. NeurIPS 2023Structured Neural Networks for Density Estimation and Causal Inference
  15. NeurIPS 2023SyncTREE: Fast Timing Analysis for Integrated Circuit Design through a Physics-informed Tree-based Graph Neural Network
  16. NeurIPS 2023Tailoring Self-Attention for Graph via Rooted Subtrees
  17. NeurIPS 2023Task-aware Distributed Source Coding under Dynamic Bandwidth
  18. NeurIPS 2023Template-free Articulated Neural Point Clouds for Reposable View Synthesis
  19. NeurIPS 2023TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
  20. NeurIPS 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphs
  21. NeurIPS 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
  22. NeurIPS 2023The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
  23. NeurIPS 2023The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
  24. NeurIPS 2023The expressive power of pooling in Graph Neural Networks
  25. NeurIPS 2023The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models
  26. NeurIPS 2023The Quantization Model of Neural Scaling
  27. NeurIPS 2023The Separation Capacity of Random Neural Networks
  28. NeurIPS 2023The Transient Nature of Emergent In-Context Learning in Transformers
  29. NeurIPS 2023Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
  30. NeurIPS 2023This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
  31. NeurIPS 2023Three Iterations of (d − 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points
  32. NeurIPS 2023TopoSRL: Topology preserving self-supervised Simplicial Representation Learning
  33. NeurIPS 2023Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs
  34. NeurIPS 2023Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
  35. NeurIPS 2023Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach
  36. NeurIPS 2023Towards Label Position Bias in Graph Neural Networks
  37. NeurIPS 2023Towards Self-Interpretable Graph-Level Anomaly Detection
  38. NeurIPS 2023Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks
  39. NeurIPS 2023Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies
  40. NeurIPS 2023Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
  41. NeurIPS 2023Training Neural Networks is NP-Hard in Fixed Dimension
  42. NeurIPS 2023Training neural operators to preserve invariant measures of chaotic attractors
  43. NeurIPS 2023Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?
  44. NeurIPS 2023Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
  45. NeurIPS 2023Transformers over Directed Acyclic Graphs
  46. NeurIPS 2023Trial matching: capturing variability with data-constrained spiking neural networks
  47. NeurIPS 2023Truly Scale-Equivariant Deep Nets with Fourier Layers
  48. NeurIPS 2023Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
  49. NeurIPS 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
  50. NeurIPS 2023Uncovering Neural Scaling Laws in Molecular Representation Learning

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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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