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 201 篇，第 4 / 5 页

151. ICLR 2023Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
152. ICLR 2023Effects of Graph Convolutions in Multi-layer Networks
153. ICLR 2023Efficient Model Updates for Approximate Unlearning of Graph-Structured Data
154. ICLR 2023Energy-based Out-of-Distribution Detection for Graph Neural Networks
155. ICLR 2023Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
156. ICLR 2023ExpressivE: A Spatio-Functional Embedding For Knowledge Graph Completion
157. ICLR 2023Fair Attribute Completion on Graph with Missing Attributes
158. ICLR 2023FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
159. ICLR 2023Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation
160. ICLR 2023Graph Contrastive Learning for Skeleton-based Action Recognition
161. ICLR 2023Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
162. ICLR 2023Graph Neural Networks for Link Prediction with Subgraph Sketching
163. ICLR 2023Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
164. ICLR 2023GReTo: Remedying dynamic graph topology-task discordance via target homophily
165. ICLR 2023Grounding Graph Network Simulators using Physical Sensor Observations
166. ICLR 2023Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
167. ICLR 2023Imitating Graph-Based Planning with Goal-Conditioned Policies
168. ICLR 2023Interpretable Geometric Deep Learning via Learnable Randomness Injection
169. ICLR 2023Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
170. ICLR 2023Latent Graph Inference using Product Manifolds
171. ICLR 2023Learnable Graph Convolutional Attention Networks
172. ICLR 2023Learnable Topological Features For Phylogenetic Inference via Graph Neural Networks
173. ICLR 2023Learning Fair Graph Representations via Automated Data Augmentations
174. ICLR 2023Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network
175. ICLR 2023Learning Hierarchical Protein Representations via Complete 3D Graph Networks
176. ICLR 2023Learning rigid dynamics with face interaction graph networks
177. ICLR 2023LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
178. ICLR 2023Limitless Stability for Graph Convolutional Networks
179. ICLR 2023Link Prediction with Non-Contrastive Learning
180. ICLR 2023LMC: Fast Training of GNNs via Subgraph Sampling with Provable Convergence
181. ICLR 2023Logical Message Passing Networks with One-hop Inference on Atomic Formulas
182. ICLR 2023MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning
183. ICLR 2023Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules
184. ICLR 2023NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
185. ICLR 2023On Compositional Uncertainty Quantification for Seq2seq Graph Parsing
186. ICLR 2023On Representing Linear Programs by Graph Neural Networks
187. ICLR 2023On Representing Mixed-Integer Linear Programs by Graph Neural Networks
188. ICLR 2023Rethinking Graph Lottery Tickets: Graph Sparsity Matters
189. ICLR 2023Rethinking the Expressive Power of GNNs via Graph Biconnectivity
190. ICLR 2023REVISITING PRUNING AT INITIALIZATION THROUGH THE LENS OF RAMANUJAN GRAPH
191. ICLR 2023Revisiting Robustness in Graph Machine Learning
192. ICLR 2023Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
193. ICLR 2023Robust Graph Dictionary Learning
194. ICLR 2023Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective
195. ICLR 2023Serving Graph Compression for Graph Neural Networks
196. ICLR 2023Specformer: Spectral Graph Neural Networks Meet Transformers
197. ICLR 2023Strategic Classification with Graph Neural Networks
198. ICLR 2023Subsampling in Large Graphs Using Ricci Curvature
199. ICLR 2023Towards Open Temporal Graph Neural Networks
200. ICLR 2023Unbiased Stochastic Proximal Solver for Graph Neural Networks with Equilibrium States

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。