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 297 篇，第 3 / 6 页

101. ICML 2025EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification
102. ICML 2025EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction
103. ICML 2025Efficient Graph Continual Learning via Lightweight Graph Neural Tangent Kernels-based Dataset Distillation
104. ICML 2025EmoGrowth: Incremental Multi-label Emotion Decoding with Augmented Emotional Relation Graph
105. ICML 2025ENAHPool: The Edge-Node Attention-based Hierarchical Pooling for Graph Neural Networks
106. ICML 2025Enhancing Graph Contrastive Learning for Protein Graphs from Perspective of Invariance
107. ICML 2025Enhancing Graph Invariant Learning from a Negative Inference Perspective
108. ICML 2025Enhancing Spectral GNNs: From Topology and Perturbation Perspectives
109. ICML 2025Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks
110. ICML 2025Equivalence is All: A Unified View for Self-supervised Graph Learning
111. ICML 2025EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
112. ICML 2025Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
113. ICML 2025FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
114. ICML 2025From Theory to Practice: Rethinking Green and Martin Kernels for Unleashing Graph Transformers
115. ICML 2025G-Adaptivity: optimised graph-based mesh relocation for finite element methods
116. ICML 2025G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
117. ICML 2025GCAL: Adapting Graph Models to Evolving Domain Shifts
118. ICML 2025Geometric Hyena Networks for Large-scale Equivariant Learning
119. ICML 2025GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning
120. ICML 2025Graph Adaptive Autoregressive Moving Average Models
121. ICML 2025Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models
122. ICML 2025Graph Generative Pre-trained Transformer
123. ICML 2025Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation
124. ICML 2025Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
125. ICML 2025Graph-Based Algorithms for Diverse Similarity Search
126. ICML 2025Graph4MM: Weaving Multimodal Learning with Structural Information
127. ICML 2025GraphGPT: Generative Pre-trained Graph Eulerian Transformer
128. ICML 2025Griffin: Towards a Graph-Centric Relational Database Foundation Model
129. ICML 2025GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
130. ICML 2025Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
131. ICML 2025Hgformer: Hyperbolic Graph Transformer for Collaborative Filtering
132. ICML 2025HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport
133. ICML 2025Hierarchical Graph Tokenization for Molecule-Language Alignment
134. ICML 2025How Expressive are Knowledge Graph Foundation Models?
135. ICML 2025How Much Can Transfer? BRIDGE: Bounded Multi-Domain Graph Foundation Model with Generalization Guarantees
136. ICML 2025Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations
137. ICML 2025HyperNear: Unnoticeable Node Injection Attacks on Hypergraph Neural Networks
138. ICML 2025Implicit degree bias in the link prediction task
139. ICML 2025Implicit Subgraph Neural Network
140. ICML 2025Improved Approximations for Hard Graph Problems using Predictions
141. ICML 2025Improved Expressivity of Hypergraph Neural Networks through High-Dimensional Generalized Weisfeiler-Leman Algorithms
142. ICML 2025Improving Soft Unification with Knowledge Graph Embedding Methods
143. ICML 2025Improving the Effective Receptive Field of Message-Passing Neural Networks
144. ICML 2025iN2V: Bringing Transductive Node Embeddings to Inductive Graphs
145. ICML 2025Instance Correlation Graph-based Naive Bayes
146. ICML 2025Large Language-Geometry Model: When LLM meets Equivariance
147. ICML 2025Learn Beneficial Noise as Graph Augmentation
148. ICML 2025Learnable Spatial-Temporal Positional Encoding for Link Prediction
149. ICML 2025Learning Latent Graph Structures and their Uncertainty
150. ICML 2025Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective

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。