深度学习 · graph neural networks论文趋势 2023–2026

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。

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 533 篇，第 8 / 11 页

351. ICML 2024Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning
352. ICML 2024Individual Fairness in Graph Decomposition
353. ICML 2024Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach
354. ICML 2024Learning Divergence Fields for Shift-Robust Graph Representations
355. ICML 2024Learning Graph Representation via Graph Entropy Maximization
356. ICML 2024Less is More: on the Over-Globalizing Problem in Graph Transformers
357. ICML 2024LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering
358. ICML 2024Modelling Microbial Communities with Graph Neural Networks
359. ICML 2024Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
360. ICML 2024Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
361. ICML 2024On the Expressive Power of Spectral Invariant Graph Neural Networks
362. ICML 2024On the Generalization of Equivariant Graph Neural Networks
363. ICML 2024On the Role of Edge Dependency in Graph Generative Models
364. ICML 2024PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
365. ICML 2024Perfect Alignment May be Poisonous to Graph Contrastive Learning
366. ICML 2024Position: Future Directions in the Theory of Graph Machine Learning
367. ICML 2024Position: Graph Foundation Models Are Already Here
368. ICML 2024Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
369. ICML 2024Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search
370. ICML 2024Quantum Positional Encodings for Graph Neural Networks
371. ICML 2024Recurrent Distance Filtering for Graph Representation Learning
372. ICML 2024Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
373. ICML 2024Robust Graph Matching when Nodes are Corrupt
374. ICML 2024S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning
375. ICML 2024Sign Rank Limitations for Inner Product Graph Decoders
376. ICML 2024Simulation of Graph Algorithms with Looped Transformers
377. ICML 2024SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter
378. ICML 2024Structure Your Data: Towards Semantic Graph Counterfactuals
379. ICML 2024Surprisingly Strong Performance Prediction with Neural Graph Features
380. ICML 2024The Expressive Power of Path-Based Graph Neural Networks
381. ICML 2024Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
382. ICML 2024Understanding Heterophily for Graph Neural Networks
383. ICML 2024Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
384. ICML 2024Unsupervised Episode Generation for Graph Meta-learning
385. ICML 2024VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
386. ICML 2023A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
387. ICML 2023A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
388. ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
389. ICML 2023A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
390. ICML 2023Adaptive Smoothing Gradient Learning for Spiking Neural Networks
391. ICML 2023Additive Causal Bandits with Unknown Graph
392. ICML 2023All in a Row: Compressed Convolution Networks for Graphs
393. ICML 2023Alternately Optimized Graph Neural Networks
394. ICML 2023Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels
395. ICML 2023Bayes-optimal Learning of Deep Random Networks of Extensive-width
396. ICML 2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
397. ICML 2023BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning
398. ICML 2023Boosting Graph Contrastive Learning via Graph Contrastive Saliency
399. ICML 2023Certified Robust Neural Networks: Generalization and Corruption Resistance
400. ICML 2023CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling

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。