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 8 / 33 页

351. ICLR 2025Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
352. ICLR 2025DUALFormer: Dual Graph Transformer
353. ICLR 2025Edge Prompt Tuning for Graph Neural Networks
354. ICLR 2025Energy-based Backdoor Defense Against Federated Graph Learning
355. ICLR 2025Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
356. ICLR 2025Factor Graph-based Interpretable Neural Networks
357. ICLR 2025Generalizing Weisfeiler-Lehman Kernels to Subgraphs
358. ICLR 2025Graph Assisted Offline-Online Deep Reinforcement Learning for Dynamic Workflow Scheduling
359. ICLR 2025Graph Neural Networks Can (Often) Count Substructures
360. ICLR 2025Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems
361. ICLR 2025Graph Transformers Dream of Electric Flow
362. ICLR 2025Graph-based Document Structure Analysis
363. ICLR 2025HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters
364. ICLR 2025Homomorphism Counts as Structural Encodings for Graph Learning
365. ICLR 2025Homomorphism Expressivity of Spectral Invariant Graph Neural Networks
366. ICLR 2025How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension
367. ICLR 2025IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning
368. ICLR 2025Improving Graph Neural Networks by Learning Continuous Edge Directions
369. ICLR 2025KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks
370. ICLR 2025Learning Graph Invariance by Harnessing Spuriosity
371. ICLR 2025Learning Graph Quantized Tokenizers
372. ICLR 2025Learning Partial Graph Matching via Optimal Partial Transport
373. ICLR 2025Learning system dynamics without forgetting
374. ICLR 2025MeshMask: Physics-Based Simulations with Masked Graph Neural Networks
375. ICLR 2025On the Completeness of Invariant Geometric Deep Learning Models
376. ICLR 2025Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
377. ICLR 2025PolyhedronNet: Representation Learning for Polyhedra with Surface-attributed Graph
378. ICLR 2025Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks
379. ICLR 2025Precedence-Constrained Winter Value for Effective Graph Data Valuation
380. ICLR 2025Quality Measures for Dynamic Graph Generative Models
381. ICLR 2025REBIND: Enhancing Ground-state Molecular Conformation Prediction via Force-Based Graph Rewiring
382. ICLR 2025Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates
383. ICLR 2025Rethinking Graph Neural Networks From A Geometric Perspective Of Node Features
384. ICLR 2025Scale-Free Graph-Language Models
385. ICLR 2025Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation
386. ICLR 2025SINGER: Stochastic Network Graph Evolving Operator for High Dimensional PDEs
387. ICLR 2025Spectro-Riemannian Graph Neural Networks
388. ICLR 2025Towards Continuous Reuse of Graph Models via Holistic Memory Diversification
389. ICLR 2025Towards Explaining the Power of Constant-depth Graph Neural Networks for Structured Linear Programming
390. ICLR 2025Training One-Dimensional Graph Neural Networks is NP-Hard
391. ICLR 2025When Graph Neural Networks Meet Dynamic Mode Decomposition
392. ICML 2025A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
393. ICML 2025A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Attention Networks
394. ICML 2025A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
395. ICML 2025A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
396. ICML 2025A Recipe for Causal Graph Regression: Confounding Effects Revisited
397. ICML 2025Adapting Precomputed Features for Efficient Graph Condensation
398. ICML 2025Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching
399. ICML 2025Adversarial Combinatorial Semi-bandits with Graph Feedback
400. ICML 2025Aggregation Buffer: Revisiting DropEdge with a New Parameter Block

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。