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 1,648 篇，第 7 / 33 页

301. NeurIPS 2026Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion
302. NeurIPS 2026PANDA: Prior-guided Attentional Dual-path Architecture
303. NeurIPS 2026PATH: A Dual Perspective for High-quality Text-attributed Graph Learning
304. NeurIPS 2026Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
305. NeurIPS 2026Pointwise Lipschitz Continuous Graph Algorithms
306. NeurIPS 2026Practical Non-Stationary Graph Gaussian Processes
307. NeurIPS 2026R-GRec: Relation-Guided Generative Recommendation via Collaborative Graph Supervision
308. NeurIPS 2026Relation-Aware Graph Foundation Model
309. NeurIPS 2026Releasing Anchors from Cross-View Correspondence: Probabilistic Multi-View Anchor Graph Clustering
310. NeurIPS 2026Return to Basics: Very Simple Graph Contrastive Learning via Noise Cancellation Principle
311. NeurIPS 2026Robust Graph Diffusion Model
312. NeurIPS 2026Robust Surrogate Modeling for Explainable Graph Neural Networks
313. NeurIPS 2026S&P: Towards Scalable and Powerful Graph Learning with Hierarchical Structural Acquisition
314. NeurIPS 2026Self-Tuning Graph Filters via State-Dependent Operator Composition
315. NeurIPS 2026Semantically Complementary Spectral Views Learning for Graph-Level Anomaly Detection
316. NeurIPS 2026SGNNBench: A Holistic Evaluation of Spiking Graph Neural Networks on Large-scale Graphs
317. NeurIPS 2026Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
318. NeurIPS 2026Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting
319. NeurIPS 2026SSDGExplainer: Structure-Semantic Dual-Guided Explainer for Graph Neural Networks
320. NeurIPS 2026Towards Universal Black-box Attacks on Graph Neural Networks
321. NeurIPS 2026UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning
322. NeurIPS 2026Uncertainty-Aware Fuzzy Graph Contrastive Learning
323. NeurIPS 2026WavCIL: Wavelet Coefficient-Domain Invariant Learning for Dynamic Graph OOD Generalization
324. NeurIPS 2026Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them
325. NeurIPS 2026When Graph Structure Provably Helps Classification: Non-Asymptotic Recovery Guarantees
326. NeurIPS 2026xVGAE: A Hierarchical Variational Graph Autoencoder for Exchangeable Graphs
327. CVPR 2025Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?
328. CVPR 2025Attribute-Missing Multi-view Graph Clustering
329. CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
330. CVPR 2025Consistent Normal Orientation for 3D Point Clouds via Least Squares on Delaunay Graph
331. CVPR 2025End-to-End HOI Reconstruction Transformer with Graph-based Encoding
332. CVPR 2025FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity
333. CVPR 2025FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts
334. CVPR 2025GG-SSMs: Graph-Generating State Space Models
335. CVPR 2025GLane3D: Detecting Lanes with Graph of 3D Keypoints
336. CVPR 2025MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images
337. CVPR 2025Mosaic of Modalities: A Comprehensive Benchmark for Multimodal Graph Learning
338. CVPR 2025NN-Former: Rethinking Graph Structure in Neural Architecture Representation
339. CVPR 2025Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping
340. CVPR 2025PHGC: Procedural Heterogeneous Graph Completion for Natural Language Task Verification in Egocentric Videos
341. CVPR 2025Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation
342. ICLR 2025Beyond Circuit Connections: A Non-Message Passing Graph Transformer Approach for Quantum Error Mitigation
343. ICLR 2025Beyond Random Masking: When Dropout meets Graph Convolutional Networks
344. ICLR 2025Bias Mitigation in Graph Diffusion Models
345. ICLR 2025Biologically Plausible Brain Graph Transformer
346. ICLR 2025BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
347. ICLR 2025Centrality-guided Pre-training for Graph
348. ICLR 2025Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs
349. ICLR 2025Deep Signature: Characterization of Large-Scale Molecular Dynamics
350. ICLR 2025Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing

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。