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 842 篇，第 17 / 17 页

801. NeurIPS 2023Towards Self-Interpretable Graph-Level Anomaly Detection
802. NeurIPS 2023Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks
803. NeurIPS 2023Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies
804. NeurIPS 2023Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
805. NeurIPS 2023Training Neural Networks is NP-Hard in Fixed Dimension
806. NeurIPS 2023Training neural operators to preserve invariant measures of chaotic attractors
807. NeurIPS 2023Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?
808. NeurIPS 2023Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
809. NeurIPS 2023Transformers over Directed Acyclic Graphs
810. NeurIPS 2023Trial matching: capturing variability with data-constrained spiking neural networks
811. NeurIPS 2023Truly Scale-Equivariant Deep Nets with Fourier Layers
812. NeurIPS 2023Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
813. NeurIPS 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
814. NeurIPS 2023Uncovering Neural Scaling Laws in Molecular Representation Learning
815. NeurIPS 2023Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions
816. NeurIPS 2023Universal Prompt Tuning for Graph Neural Networks
817. NeurIPS 2023Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
818. NeurIPS 2023Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization
819. NeurIPS 2023UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures
820. NeurIPS 2023Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision
821. NeurIPS 2023Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation
822. NeurIPS 2023UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
823. NeurIPS 2023V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs
824. NeurIPS 2023Variational Annealing on Graphs for Combinatorial Optimization
825. NeurIPS 2023Variational Monte Carlo on a Budget — Fine-tuning pre-trained Neural Wavefunctions
826. NeurIPS 2023VeriX: Towards Verified Explainability of Deep Neural Networks
827. NeurIPS 2023Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements
828. NeurIPS 2023Volume Feature Rendering for Fast Neural Radiance Field Reconstruction
829. NeurIPS 2023WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding
830. NeurIPS 2023Wasserstein distributional robustness of neural networks
831. NeurIPS 2023Weakly Coupled Deep Q-Networks
832. NeurIPS 2023What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
833. NeurIPS 2023What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models
834. NeurIPS 2023What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement.
835. NeurIPS 2023What Planning Problems Can A Relational Neural Network Solve?
836. NeurIPS 2023When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability
837. NeurIPS 2023When Do Neural Nets Outperform Boosted Trees on Tabular Data?
838. NeurIPS 2023White-Box Transformers via Sparse Rate Reduction
839. NeurIPS 2023Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
840. NeurIPS 2023Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models
841. NeurIPS 2023Window-Based Distribution Shift Detection for Deep Neural Networks
842. NeurIPS 2023Zero-One Laws of 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。