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 9 / 15 页

  1. NeurIPS 2023Explain Any Concept: Segment Anything Meets Concept-Based Explanation
  2. NeurIPS 2023Explainable Brain Age Prediction using coVariance Neural Networks
  3. NeurIPS 2023Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
  4. NeurIPS 2023Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
  5. NeurIPS 2023Expressive probabilistic sampling in recurrent neural networks
  6. NeurIPS 2023Expressive Sign Equivariant Networks for Spectral Geometric Learning
  7. NeurIPS 2023Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
  8. NeurIPS 2023Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
  9. NeurIPS 2023Fair Graph Distillation
  10. NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
  11. NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
  12. NeurIPS 2023Federated Learning via Meta-Variational Dropout
  13. NeurIPS 2023FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
  14. NeurIPS 2023Fine-grained Expressivity of Graph Neural Networks
  15. NeurIPS 2023Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs
  16. NeurIPS 2023Flat Seeking Bayesian Neural Networks
  17. NeurIPS 2023FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow
  18. NeurIPS 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
  19. NeurIPS 2023FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow
  20. NeurIPS 2023Fragment-based Pretraining and Finetuning on Molecular Graphs
  21. NeurIPS 2023From Tempered to Benign Overfitting in ReLU Neural Networks
  22. NeurIPS 2023Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
  23. NeurIPS 2023Full-Atom Protein Pocket Design via Iterative Refinement
  24. NeurIPS 2023Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
  25. NeurIPS 2023Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
  26. NeurIPS 2023Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
  27. NeurIPS 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
  28. NeurIPS 2023GALOPA: Graph Transport Learning with Optimal Plan Alignment
  29. NeurIPS 2023Generalised f-Mean Aggregation for Graph Neural Networks
  30. NeurIPS 2023Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
  31. NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
  32. NeurIPS 2023Generating Behaviorally Diverse Policies with Latent Diffusion Models
  33. NeurIPS 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEs
  34. NeurIPS 2023GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
  35. NeurIPS 2023GEQ: Gaussian Kernel Inspired Equilibrium Models
  36. NeurIPS 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
  37. NeurIPS 2023GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
  38. NeurIPS 2023Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization
  39. NeurIPS 2023Globally injective and bijective neural operators
  40. NeurIPS 2023GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
  41. NeurIPS 2023Goal-conditioned Offline Planning from Curious Exploration
  42. NeurIPS 2023Going beyond persistent homology using persistent homology
  43. NeurIPS 2023GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
  44. NeurIPS 2023Gradient-Based Feature Learning under Structured Data
  45. NeurIPS 2023Graph Clustering with Graph Neural Networks
  46. NeurIPS 2023Graph Contrastive Learning with Stable and Scalable Spectral Encoding
  47. NeurIPS 2023Graph Convolutional Kernel Machine versus Graph Convolutional Networks
  48. NeurIPS 2023Graph Denoising Diffusion for Inverse Protein Folding
  49. NeurIPS 2023Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
  50. NeurIPS 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis

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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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