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 23 / 33 页

1101. ICML 2023Minimal Width for Universal Property of Deep RNN
1102. ICML 2023Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
1103. ICML 2023Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
1104. ICML 2023Moccasin: Efficient Tensor Rematerialization for Neural Networks
1105. ICML 2023Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
1106. ICML 2023MODeL: Memory Optimizations for Deep Learning
1107. ICML 2023Multi-class Graph Clustering via Approximated Effective $p$-Resistance
1108. ICML 2023Neural networks trained with SGD learn distributions of increasing complexity
1109. ICML 2023Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks
1110. ICML 2023NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes
1111. ICML 2023Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
1112. ICML 2023On the Connection Between MPNN and Graph Transformer
1113. ICML 2023On the Expressive Power of Geometric Graph Neural Networks
1114. ICML 2023On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization
1115. ICML 2023On the Initialization of Graph Neural Networks
1116. ICML 2023Optimizing NOTEARS Objectives via Topological Swaps
1117. ICML 2023Overcoming Simplicity Bias in Deep Networks using a Feature Sieve
1118. ICML 2023Path Neural Networks: Expressive and Accurate Graph Neural Networks
1119. ICML 2023Personalized Subgraph Federated Learning
1120. ICML 2023Perturbation Analysis of Neural Collapse
1121. ICML 2023Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
1122. ICML 2023Quantitative Universal Approximation Bounds for Deep Belief Networks
1123. ICML 2023Randomized Schur Complement Views for Graph Contrastive Learning
1124. ICML 2023Relevant Walk Search for Explaining Graph Neural Networks
1125. ICML 2023Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
1126. ICML 2023Revisiting Structured Variational Autoencoders
1127. ICML 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networks
1128. ICML 2023RGE: A Repulsive Graph Rectification for Node Classification via Influence
1129. ICML 2023RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
1130. ICML 2023Scaling Spherical CNNs
1131. ICML 2023Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
1132. ICML 2023SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching
1133. ICML 2023SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
1134. ICML 2023SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
1135. ICML 2023SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
1136. ICML 2023Spatial Implicit Neural Representations for Global-Scale Species Mapping
1137. ICML 2023Special Properties of Gradient Descent with Large Learning Rates
1138. ICML 2023spred: Solving L1 Penalty with SGD
1139. ICML 2023Statistical Foundations of Prior-Data Fitted Networks
1140. ICML 2023StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
1141. ICML 2023Subequivariant Graph Reinforcement Learning in 3D Environments
1142. ICML 2023Taming graph kernels with random features
1143. ICML 2023The Acquisition of Physical Knowledge in Generative Neural Networks
1144. ICML 2023The Hessian perspective into the Nature of Convolutional Neural Networks
1145. ICML 2023Total Variation Graph Neural Networks
1146. ICML 2023Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
1147. ICML 2023Towards Deep Attention in Graph Neural Networks: Problems and Remedies
1148. ICML 2023Towards Robust Graph Incremental Learning on Evolving Graphs
1149. ICML 2023Towards Understanding Generalization of Graph Neural Networks
1150. ICML 2023Trompt: Towards a Better Deep Neural Network for Tabular Data

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。