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 297 篇，第 4 / 6 页

151. NeurIPS 2024Any2Graph: Deep End-To-End Supervised Graph Prediction With An Optimal Transport Loss
152. NeurIPS 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learning
153. NeurIPS 2024Are Graph Neural Networks Optimal Approximation Algorithms?
154. NeurIPS 2024Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
155. NeurIPS 2024Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
156. NeurIPS 2024Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning
157. NeurIPS 2024Boosting Graph Pooling with Persistent Homology
158. NeurIPS 2024Can Graph Learning Improve Planning in LLM-based Agents?
159. NeurIPS 2024Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
160. NeurIPS 2024Challenges of Generating Structurally Diverse Graphs
161. NeurIPS 2024Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention
162. NeurIPS 2024Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding
163. NeurIPS 2024Continuous Partitioning for Graph-Based Semi-Supervised Learning
164. NeurIPS 2024Continuous Product Graph Neural Networks
165. NeurIPS 2024DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach
166. NeurIPS 2024Deep Equilibrium Algorithmic Reasoning
167. NeurIPS 2024Deep Graph Mating
168. NeurIPS 2024Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module
169. NeurIPS 2024Deep Homomorphism Networks
170. NeurIPS 2024DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
171. NeurIPS 2024DiGRAF: Diffeomorphic Graph-Adaptive Activation Function
172. NeurIPS 2024DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis
173. NeurIPS 2024Dissecting the Failure of Invariant Learning on Graphs
174. NeurIPS 2024Distributed-Order Fractional Graph Operating Network
175. NeurIPS 2024DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
176. NeurIPS 2024Dynamic Rescaling for Training GNNs
177. NeurIPS 2024Efficient Graph Matching for Correlated Stochastic Block Models
178. NeurIPS 2024EGonc : Energy-based Open-Set Node Classification with substitute Unknowns
179. NeurIPS 2024EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection
180. NeurIPS 2024Energy-based Epistemic Uncertainty for Graph Neural Networks
181. NeurIPS 2024Enhancing Chess Reinforcement Learning with Graph Representation
182. NeurIPS 2024Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
183. NeurIPS 2024Enhancing Robustness of Graph Neural Networks on Social Media with Explainable Inverse Reinforcement Learning
184. NeurIPS 2024Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
185. NeurIPS 2024Even Sparser Graph Transformers
186. NeurIPS 2024Expected Probabilistic Hierarchies
187. NeurIPS 2024Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering
188. NeurIPS 2024Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks
189. NeurIPS 2024Extracting Training Data from Molecular Pre-trained Models
190. NeurIPS 2024Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
191. NeurIPS 2024Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification
192. NeurIPS 2024Faster Local Solvers for Graph Diffusion Equations
193. NeurIPS 2024Federated Graph Learning for Cross-Domain Recommendation
194. NeurIPS 2024FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
195. NeurIPS 2024FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features
196. NeurIPS 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
197. NeurIPS 2024GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
198. NeurIPS 2024Generalizing CNNs to graphs with learnable neighborhood quantization
199. NeurIPS 2024Generative Semi-supervised Graph Anomaly Detection
200. NeurIPS 2024Geometric Trajectory Diffusion 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。