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
- 深度学习 · graph neural networks论文从 2023 年的 728 篇降到 2026 年的 326 篇，热度在回落。
- NeurIPS 是发深度学习 · graph neural networks论文最多的会议，2023–2026 年共 842 篇，占四个会议总量的 51%。
- 交叉方向里涨得最快的是数据集与基准：同时属于两个主题的论文从 2023 年的 9 篇增至 2026 年的 20 篇。
- 在「深度学习」的 11 个主题里，它的 2026 年论文数排第 5；同组增长最快的扩散模型是 4.1 倍，深度学习 · graph neural networks是 0.4 倍。
各会议论文数
按年份和会议统计
| 年份 | NeurIPS | ICLR | ICML | CVPR | 合计 |
|---|---|---|---|---|---|
| 2023 | 489 | 68 | 148 | 23 | 728 |
| 2024 | 160 | 51 | 76 | 10 | 297 |
| 2025 | 91 | 50 | 141 | 15 | 297 |
| 2026 | 102 | 32 | 168 | 24 | 326 |
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
- NeurIPS 2026A New Perspective on Target-Conditioned Structural Dynamics for Link Prediction in Dynamic Graphs
- ICML 2026A Graph Foundation Model with Cross-Modal Alignment and Modality-Aware Expert Fusion for Multi-Modal Graphs
- ICLR 2026Laplacian Kernelized Bandit
- CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
- NeurIPS 2025Depth-Width Tradeoffs for Transformers on Graph Tasks
- ICML 2025A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
- ICLR 2025Factor Graph-based Interpretable Neural Networks
- CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
- NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
- ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
- ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
- CVPR 2024Learning Structure-from-Motion with Graph Attention Networks
- NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
- ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
- ICLR 2023Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
- CVPR 2023An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity
- NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
- ICML 2026A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
- ICLR 2026GTool: Graph Enhanced Tool Planning with Large Language Model
- CVPR 2026GSNR Graph Smooth Null-Space Representation for Inverse Problems
全部深度学习 · graph neural networks论文
共 728 篇，第 5 / 15 页
- ICML 2023Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
- ICML 2023Quantitative Universal Approximation Bounds for Deep Belief Networks
- ICML 2023Randomized Schur Complement Views for Graph Contrastive Learning
- ICML 2023Relevant Walk Search for Explaining Graph Neural Networks
- ICML 2023Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
- ICML 2023Revisiting Structured Variational Autoencoders
- ICML 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networks
- ICML 2023RGE: A Repulsive Graph Rectification for Node Classification via Influence
- ICML 2023RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
- ICML 2023Scaling Spherical CNNs
- ICML 2023Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
- ICML 2023SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching
- ICML 2023SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
- ICML 2023SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
- ICML 2023SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
- ICML 2023Spatial Implicit Neural Representations for Global-Scale Species Mapping
- ICML 2023Special Properties of Gradient Descent with Large Learning Rates
- ICML 2023spred: Solving L1 Penalty with SGD
- ICML 2023Statistical Foundations of Prior-Data Fitted Networks
- ICML 2023StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
- ICML 2023Subequivariant Graph Reinforcement Learning in 3D Environments
- ICML 2023Taming graph kernels with random features
- ICML 2023The Acquisition of Physical Knowledge in Generative Neural Networks
- ICML 2023The Hessian perspective into the Nature of Convolutional Neural Networks
- ICML 2023Total Variation Graph Neural Networks
- ICML 2023Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
- ICML 2023Towards Deep Attention in Graph Neural Networks: Problems and Remedies
- ICML 2023Towards Robust Graph Incremental Learning on Evolving Graphs
- ICML 2023Towards Understanding Generalization of Graph Neural Networks
- ICML 2023Trompt: Towards a Better Deep Neural Network for Tabular Data
- ICML 2023Under-Counted Tensor Completion with Neural Incorporation of Attributes
- ICML 2023Understanding Oversquashing in GNNs through the Lens of Effective Resistance
- ICML 2023Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data
- ICML 2023Vertical Federated Graph Neural Network for Recommender System
- ICML 2023Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks
- ICML 2023What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
- ICML 2023When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
- ICML 2023Width and Depth Limits Commute in Residual Networks
- ICML 2023XAI Beyond Classification: Interpretable Neural Clustering
- NeurIPS 2023[Re] $\mathcal{G}$-Mixup: Graph Data Augmentation for Graph Classification
- NeurIPS 2023[Re] End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
- NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
- NeurIPS 2023[Re] Variational Neural Cellular Automata
- NeurIPS 2023A Bayesian Take on Gaussian Process Networks
- NeurIPS 2023A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
- NeurIPS 2023A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains
- NeurIPS 2023A fast heuristic to optimize time-space tradeoff for large models
- NeurIPS 2023A Fractional Graph Laplacian Approach to Oversmoothing
- NeurIPS 2023A General Framework for Equivariant Neural Networks on Reductive Lie Groups
- NeurIPS 2023A General Theory of Correct, Incorrect, and Extrinsic Equivariance
在 Claude、Cursor 里直接问这个趋势
接上 Paperlist MCP 后，写综述、挑方向或准备投稿时，不用离开对话框就能拿到这页的数字：每年各会议的论文数、增长倍数、涨得最快的子方向和代表论文，AI 会按你的问题整理。
可以直接复制这句话问 AI
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"}
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。
「深度学习」下的其他主题
- 扩散模型 6,900
- 深度学习 · generative models and autoencoders 4,314
- 深度学习 · large language models 2,334
- 深度学习 · self supervised learning 1,958
- 深度学习 · optimization 953
- 深度学习 · representation learning 802
- 深度学习 · probabilistic methods 637
- 深度学习 · foundation models 536
- 深度学习 · causal inference 316
- 深度学习 · transfer learning 197