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 1,648 篇，第 14 / 33 页

  1. ICLR 2024Graph Parsing Networks
  2. ICLR 2024Graph Transformers on EHRs: Better Representation Improves Downstream Performance
  3. ICLR 2024GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks
  4. ICLR 2024GraphPulse: Topological representations for temporal graph property prediction
  5. ICLR 2024GTMGC: Using Graph Transformer to Predict Molecule’s Ground-State Conformation
  6. ICLR 2024HiGen: Hierarchical Graph Generative Networks
  7. ICLR 2024Hybrid Directional Graph Neural Network for Molecules
  8. ICLR 2024iGraphMix: Input Graph Mixup Method for Node Classification
  9. ICLR 2024InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior
  10. ICLR 2024Learning Adaptive Multiresolution Transforms via Meta-Framelet-based Graph Convolutional Network
  11. ICLR 2024Learning to solve Class-Constrained Bin Packing Problems via Encoder-Decoder Model
  12. ICLR 2024Local Graph Clustering with Noisy Labels
  13. ICLR 2024Locality-Aware Graph Rewiring in GNNs
  14. ICLR 2024MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy
  15. ICLR 2024Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
  16. ICLR 2024One For All: Towards Training One Graph Model For All Classification Tasks
  17. ICLR 2024Orbit-Equivariant Graph Neural Networks
  18. ICLR 2024PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters
  19. ICLR 2024Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
  20. ICLR 2024PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks
  21. ICLR 2024Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks
  22. ICLR 2024Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
  23. ICLR 2024Robust Angular Synchronization via Directed Graph Neural Networks
  24. ICLR 2024SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
  25. ICLR 2024StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning
  26. ICLR 2024TEDDY: Trimming Edges with Degree-based Discrimination Strategy
  27. ICLR 2024Temporal Generalization Estimation in Evolving Graphs
  28. ICLR 2024Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
  29. ICLR 2024Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
  30. ICLR 2024Training Graph Transformers via Curriculum-Enhanced Attention Distillation
  31. ICLR 2024Uncertainty-aware Graph-based Hyperspectral Image Classification
  32. ICLR 2024Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
  33. ICLR 2024Variance-enlarged Poisson Learning for Graph-based Semi-Supervised Learning with Extremely Sparse Labeled Data
  34. ICLR 2024VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
  35. ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
  36. ICML 2024A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering
  37. ICML 2024An Efficient Maximal Ancestral Graph Listing Algorithm
  38. ICML 2024CKGConv: General Graph Convolution with Continuous Kernels
  39. ICML 2024Class-Imbalanced Graph Learning without Class Rebalancing
  40. ICML 2024Collective Certified Robustness against Graph Injection Attacks
  41. ICML 2024Community-Invariant Graph Contrastive Learning
  42. ICML 2024Comparing Graph Transformers via Positional Encodings
  43. ICML 2024Cooperative Graph Neural Networks
  44. ICML 2024Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization
  45. ICML 2024Editing Partially Observable Networks via Graph Diffusion Models
  46. ICML 2024Effective Federated Graph Matching
  47. ICML 2024Empowering Graph Invariance Learning with Deep Spurious Infomax
  48. ICML 2024Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
  49. ICML 2024EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
  50. ICML 2024Explaining Graph Neural Networks via Structure-aware Interaction Index

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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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