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 8 / 17 页

  1. NeurIPS 2024What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks
  2. NeurIPS 2024What Matters in Graph Class Incremental Learning? An Information Preservation Perspective
  3. NeurIPS 2024Why the Metric Backbone Preserves Community Structure
  4. NeurIPS 2023[Re] $\mathcal{G}$-Mixup: Graph Data Augmentation for Graph Classification
  5. NeurIPS 2023[Re] End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
  6. NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
  7. NeurIPS 2023[Re] Variational Neural Cellular Automata
  8. NeurIPS 2023A Bayesian Take on Gaussian Process Networks
  9. NeurIPS 2023A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
  10. NeurIPS 2023A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains
  11. NeurIPS 2023A fast heuristic to optimize time-space tradeoff for large models
  12. NeurIPS 2023A Fractional Graph Laplacian Approach to Oversmoothing
  13. NeurIPS 2023A General Framework for Equivariant Neural Networks on Reductive Lie Groups
  14. NeurIPS 2023A General Theory of Correct, Incorrect, and Extrinsic Equivariance
  15. NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
  16. NeurIPS 2023A graphon-signal analysis of graph neural networks
  17. NeurIPS 2023A Metadata-Driven Approach to Understand Graph Neural Networks
  18. NeurIPS 2023A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
  19. NeurIPS 2023A new perspective on building efficient and expressive 3D equivariant graph neural networks
  20. NeurIPS 2023A polar prediction model for learning to represent visual transformations
  21. NeurIPS 2023A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation
  22. NeurIPS 2023A Scalable Neural Network for DSIC Affine Maximizer Auction Design
  23. NeurIPS 2023A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time
  24. NeurIPS 2023A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
  25. NeurIPS 2023Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism
  26. NeurIPS 2023Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation
  27. NeurIPS 2023Accelerating Molecular Graph Neural Networks via Knowledge Distillation
  28. NeurIPS 2023Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement
  29. NeurIPS 2023Active Learning-Based Species Range Estimation
  30. NeurIPS 2023Active Negative Loss Functions for Learning with Noisy Labels
  31. NeurIPS 2023Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
  32. NeurIPS 2023Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds
  33. NeurIPS 2023Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
  34. NeurIPS 2023Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
  35. NeurIPS 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
  36. NeurIPS 2023Affinity-Aware Graph Networks
  37. NeurIPS 2023AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
  38. NeurIPS 2023Aiming towards the minimizers: fast convergence of SGD for overparametrized problems
  39. NeurIPS 2023AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity
  40. NeurIPS 2023An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations
  41. NeurIPS 2023An Inductive Bias for Tabular Deep Learning
  42. NeurIPS 2023An Information Theory Perspective on Variance-Invariance-Covariance Regularization
  43. NeurIPS 2023Analysis of Variance of Multiple Causal Networks
  44. NeurIPS 2023Analyzing Generalization of Neural Networks through Loss Path Kernels
  45. NeurIPS 2023Anchor Data Augmentation
  46. NeurIPS 2023Approximately Equivariant Graph Networks
  47. NeurIPS 2023Approximation-Generalization Trade-offs under (Approximate) Group Equivariance
  48. NeurIPS 2023Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
  49. NeurIPS 2023Are aligned neural networks adversarially aligned?
  50. NeurIPS 2023Are GATs Out of Balance?

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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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