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 533 篇，第 2 / 11 页

51. ICML 2026Error-Driven Graph Augmentation for Mesh-Based PDE Surrogates
52. ICML 2026Explicitly Modeling Censoring Produces Superior Survival Predictors
53. ICML 2026Exploring Motif-based Heterogeneous Graph Learning for ReDoS Detection
54. ICML 2026Expressive Graph Neural Networks via Equivariant Use of Noise
55. ICML 2026Fast Mixture of Curvature-Aware Experts for Diverse and Dynamic Graph Topologies
56. ICML 2026Feature-Aware (Hyper)graph Generation via Next-Scale Prediction
57. ICML 2026Federated Graph Learning via Structure-Aware Fusion Using a Kalman Framework with Learnable Dynamics
58. ICML 2026FedRGL: Robust Federated Graph Learning under Label Noise
59. ICML 2026Feedback Control for Multi-Objective Graph Self-Supervision
60. ICML 2026Fixed Aggregation Features Can Rival GNNs
61. ICML 2026From Distribution to Geometry: Stable Graph Generalization via Invariant Barycenters
62. ICML 2026From Moments to Models: Graphon-Mixture Learning for Mixup and Contrastive Learning
63. ICML 2026Full-Spectrum Graph Neural Networks: Expressive and Scalable
64. ICML 2026Gauge-Equivariant Graph Networks via Self-Interference Cancellation
65. ICML 2026Generalist Graph Anomaly Detection via Prototype-Based Distillation
66. ICML 2026Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion
67. ICML 2026GI-GCN: Global Interacted Graph Convolutional Networks via Dominant Sets for Graph Classification
68. ICML 2026GOCM: Single-Step Graph Outlier Synthesis via Origin Consistency Model
69. ICML 2026GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
70. ICML 2026Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
71. ICML 2026Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings
72. ICML 2026Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication
73. ICML 2026Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
74. ICML 2026Graph is a Substrate Across Data Modalities
75. ICML 2026Graph Neural Dynamics via Learned Energy and Tangential Flows
76. ICML 2026Graph Neural Networks Are Not Continuous Across Graph Resolutions
77. ICML 2026Graph-GRPO: Training Graph Flow Models with Reinforcement Learning
78. ICML 2026GraphFLEx: Unsupervised Structure Learning $\underline{\text{F}}$ramework for $\underline{\text{L}}$arge $\underline{\text{Ex}}$panding $\underline{\text{Graph}}$s
79. ICML 2026GraphFlow: A Graph-Based Workflow Management for Efficient LLM-Agent Serving
80. ICML 2026GraphP-FL: Personalized Federated Graph Learning via Dynamic Structure Awareness and Fisher Information Elastic Alignment
81. ICML 2026GraphPFN: A Prior-Data Fitted Graph Foundation Model
82. ICML 2026H$^2$CL: Heterogeneity-Aware Hypergraph Contrastive Learning for Robust Representation Learning
83. ICML 2026Hierarchical Multi Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation
84. ICML 2026High-Fidelity ANN-to-SNN Conversion via Closed-Loop CKA Distillation
85. ICML 2026HSMAD: Heterophily-Driven Spectral and Manifold Learning for Graph Anomaly Detection
86. ICML 2026Identifiable Equivariant Networks are Layerwise Equivariant
87. ICML 2026Identifying and Correcting Label Noise for Robust GNNs via Influence Contradiction
88. ICML 2026INFER: Learning Implicit Neural Frequency Response Fields for Confined Acoustic Environments
89. ICML 2026InvGNN: Learning Invertible Node Representations on Graphs
90. ICML 2026Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning
91. ICML 2026L2G-NET: Local to Global Spectral Graph Neural Networks via Cauchy Factorizations
92. ICML 2026Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection
93. ICML 2026Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision
94. ICML 2026Learning Graph Foundation Models on Riemannian Graph-of-Graphs
95. ICML 2026Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
96. ICML 2026Learning Molecular Semantic Invariant Representation with Prototype Constraint
97. ICML 2026Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining
98. ICML 2026Learning to Approximate Uniform Facility Location via Graph Neural Networks
99. ICML 2026Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
100. ICML 2026MC-HNN: Learning Latent Structural Semantics and High-Rank Representations for Hypergraph Neural Networks

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。