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 141 篇，第 3 / 3 页

101. ICML 2025Primal-Dual Neural Algorithmic Reasoning
102. ICML 2025Primphormer: Efficient Graph Transformers with Primal Representations
103. ICML 2025Propagate and Inject: Revisiting Propagation-Based Feature Imputation for Graphs with Partially Observed Features
104. ICML 2025Pruning for GNNs: Lower Complexity with Comparable Expressiveness
105. ICML 2025Redundancy Undermines the Trustworthiness of Self-Interpretable GNNs
106. ICML 2025RelGNN: Composite Message Passing for Relational Deep Learning
107. ICML 2025ResearchTown: Simulator of Human Research Community
108. ICML 2025Rethinking Time Encoding via Learnable Transformation Functions
109. ICML 2025Rhomboid Tiling for Geometric Graph Deep Learning
110. ICML 2025SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion
111. ICML 2025Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation
112. ICML 2025Scalable Sobolev IPM for Probability Measures on a Graph
113. ICML 2025SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval
114. ICML 2025SHARP-Distill: A 68× Faster Recommender System with Hypergraph Neural Networks and Language Models
115. ICML 2025Signed Laplacians for Constrained Graph Clustering
116. ICML 2025Simple Path Structural Encoding for Graph Transformers
117. ICML 2025Smooth Interpolation for Improved Discrete Graph Generative Models
118. ICML 2025SPHINX: Structural Prediction using Hypergraph Inference Network
119. ICML 2025Stability and Generalization Capability of Subgraph Reasoning Models for Inductive Knowledge Graph Completion
120. ICML 2025Stable Fair Graph Representation Learning with Lipschitz Constraint
121. ICML 2025Structure Is All You Need: Structural Representation Learning on Hyper-Relational Knowledge Graphs
122. ICML 2025Supercharging Graph Transformers with Advective Diffusion
123. ICML 2025Telling Peer Direct Effects from Indirect Effects in Observational Network Data
124. ICML 2025Test-Time Graph Neural Dataset Search With Generative Projection
125. ICML 2025The Generalized Skew Spectrum of Graphs
126. ICML 2025The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products
127. ICML 2025Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks
128. ICML 2025TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction
129. ICML 2025TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
130. ICML 2025Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
131. ICML 2025TopoTune: A Framework for Generalized Combinatorial Complex Neural Networks
132. ICML 2025Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
133. ICML 2025Towards characterizing the value of edge embeddings in Graph Neural Networks
134. ICML 2025Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory
135. ICML 2025Unifews: You Need Fewer Operations for Efficient Graph Neural Networks
136. ICML 2025Vision Graph Prompting via Semantic Low-Rank Decomposition
137. ICML 2025Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win
138. ICML 2025What Makes a Good Feedforward Computational Graph?
139. ICML 2025When Do LLMs Help With Node Classification? A Comprehensive Analysis
140. ICML 2025WILTing Trees: Interpreting the Distance Between MPNN Embeddings
141. ICML 2025Zero-Shot Generalization of GNNs over Distinct Attribute Domains

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。