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 489 篇，第 4 / 10 页

  1. NeurIPS 2023Epistemic Neural Networks
  2. NeurIPS 2023Equivariant Neural Operator Learning with Graphon Convolution
  3. NeurIPS 2023Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
  4. NeurIPS 2023ESSEN: Improving Evolution State Estimation for Temporal Networks using Von Neumann Entropy
  5. NeurIPS 2023Euler-Lagrange Analysis of Generative Adversarial Networks
  6. NeurIPS 2023Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
  7. NeurIPS 2023Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis
  8. NeurIPS 2023Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
  9. NeurIPS 2023Evolving Connectivity for Recurrent Spiking Neural Networks
  10. NeurIPS 2023Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
  11. NeurIPS 2023Exact Verification of ReLU Neural Control Barrier Functions
  12. NeurIPS 2023Explain Any Concept: Segment Anything Meets Concept-Based Explanation
  13. NeurIPS 2023Explainable Brain Age Prediction using coVariance Neural Networks
  14. NeurIPS 2023Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
  15. NeurIPS 2023Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
  16. NeurIPS 2023Expressive probabilistic sampling in recurrent neural networks
  17. NeurIPS 2023Expressive Sign Equivariant Networks for Spectral Geometric Learning
  18. NeurIPS 2023Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
  19. NeurIPS 2023Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
  20. NeurIPS 2023Fair Graph Distillation
  21. NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
  22. NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
  23. NeurIPS 2023Federated Learning via Meta-Variational Dropout
  24. NeurIPS 2023FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
  25. NeurIPS 2023Fine-grained Expressivity of Graph Neural Networks
  26. NeurIPS 2023Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs
  27. NeurIPS 2023Flat Seeking Bayesian Neural Networks
  28. NeurIPS 2023FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow
  29. NeurIPS 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
  30. NeurIPS 2023FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow
  31. NeurIPS 2023Fragment-based Pretraining and Finetuning on Molecular Graphs
  32. NeurIPS 2023From Tempered to Benign Overfitting in ReLU Neural Networks
  33. NeurIPS 2023Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
  34. NeurIPS 2023Full-Atom Protein Pocket Design via Iterative Refinement
  35. NeurIPS 2023Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
  36. NeurIPS 2023Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
  37. NeurIPS 2023Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
  38. NeurIPS 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
  39. NeurIPS 2023GALOPA: Graph Transport Learning with Optimal Plan Alignment
  40. NeurIPS 2023Generalised f-Mean Aggregation for Graph Neural Networks
  41. NeurIPS 2023Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
  42. NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
  43. NeurIPS 2023Generating Behaviorally Diverse Policies with Latent Diffusion Models
  44. NeurIPS 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEs
  45. NeurIPS 2023GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
  46. NeurIPS 2023GEQ: Gaussian Kernel Inspired Equilibrium Models
  47. NeurIPS 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
  48. NeurIPS 2023GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
  49. NeurIPS 2023Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization
  50. NeurIPS 2023Globally injective and bijective neural operators

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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？

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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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