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 326 篇，第 4 / 7 页

151. ICML 2026Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
152. ICML 2026Learning Molecular Semantic Invariant Representation with Prototype Constraint
153. ICML 2026Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining
154. ICML 2026Learning to Approximate Uniform Facility Location via Graph Neural Networks
155. ICML 2026Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
156. ICML 2026MC-HNN: Learning Latent Structural Semantics and High-Rank Representations for Hypergraph Neural Networks
157. ICML 2026MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
158. ICML 2026Message Passing on the Edge: Towards Scalable and Expressive GNNs
159. ICML 2026Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective
160. ICML 2026MIMO-LP: A Multi-Input Multi-Output Framework for Subgraph-based Link Prediction
161. ICML 2026Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection
162. ICML 2026Multi-scale Explainer for Graph Neural Networks
163. ICML 2026MUSA-PINN: Multi-scale Weak-form Physics-Informed Neural Networks for Fluid Flow in Complex Geometries
164. ICML 2026MV-FGAD: Towards Efficient and Effective Federated Graph Anomaly Detection via Multi-view Learning
165. ICML 2026NetDiff: Graph Diffusion with Improved Global Capabilities to Generate and Update Mobile Network Topologies
166. ICML 2026Normality Calibration in Semi-supervised Graph Anomaly Detection
167. ICML 2026On the Expressive Power of GNNs to Solve Linear SDPs
168. ICML 2026On the Spectral Unreachability of Brain Graph Learning
169. ICML 2026One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
170. ICML 2026OpenMAG: A Comprehensive Benchmark for Multimodal-Attributed Graph
171. ICML 2026PhenoBrain: Phenotype-Conditioned Long-Range Communication for Multi-Modal Brain Network Analysis
172. ICML 2026Plain Transformers are Surprisingly Powerful Link Predictors
173. ICML 2026PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs
174. ICML 2026PSG-Nav: Probabilistic Scene Graph Navigation via Multiverse Decision Making
175. ICML 2026pTNAS: Progressive Neural Architecture Search for Tabular Data
176. ICML 2026Quantile-Free Uncertainty Quantification in Graph Neural Networks
177. ICML 2026Quantum Algorithms for Triangle Cut Sparsification
178. ICML 2026RADE: Random Add-Drop Edge as a Regularizer
179. ICML 2026RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections
180. ICML 2026Reconstruction Outcomes Look Similar but Processes Differ: Improving Context Consistency and Coverage in Graph Masked Auto-Encoder
181. ICML 2026Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases
182. ICML 2026Rethinking Contrastive Learning for Graph Collaborative Filtering: Limitations and a Simple Remedy
183. ICML 2026Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
184. ICML 2026Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution
185. ICML 2026Rethinking Graph Transformers as Graph Signal Denoisers: The Role of Block-Diagonal Priors
186. ICML 2026Rethinking Parameter Sharing as Graph Coloring for Structured Compression
187. ICML 2026Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
188. ICML 2026Revisiting Pre-Propagation GNNs: Robust Diffusion Operators and Hidden-State Re-Propagation
189. ICML 2026RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs
190. ICML 2026S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
191. ICML 2026Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
192. ICML 2026SC-FAGC: Size Constrained Fast Anchor-based Graph Clustering
193. ICML 2026Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms
194. ICML 2026SFCLTA: Spectral Fusion Contrastive Learning with Topology-Adaptive Graph Augmentation
195. ICML 2026Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
196. ICML 2026SI-IGCL: Subject Invariance-aware Inverse Graph Contrastive Learning for Psychiatric Disorder Identification
197. ICML 2026SimGFM: Simplifying Discrete Flow Matching for Graph Generation
198. ICML 2026Size Transferability of Graph Convolutional Networks across Sparsity: A Generalized Graphon Perspective
199. ICML 2026Smoothness Errors in Dynamics Models and How to Avoid Them
200. ICML 2026SpaEF: Spatially Resolved Transcriptomics Data Element-Wise Denoising Framework Powered by Large Models

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。