Graph Neural Networks papers, 2023–2026

Key findings

• Graph Neural Networks fell from 434 papers in 2023 to 183 in 2026, so interest is cooling.
• NeurIPS publishes the most Graph Neural Networks work: 489 papers from 2023 to 2026, 47% of the four-venue total.
• The fastest-growing overlap is with Datasets and Benchmarks: papers tagged with both rose from 10 in 2023 to 10 in 2026.
• Among the 5 topics under Graph and Structured Data, it ranks #4 by 2026 paper count; the fastest grower in the group, Time Series, is up 3.2x versus 0.4x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry
• ICML 2026Aitchison Embeddings for Learning Compositional Graph Representations
• ICLR 2026Certified Evaluation of Model-Level Explanations for Graph Neural Networks
• CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
• NeurIPS 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework
• ICML 2025Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts
• ICLR 2025Edge Prompt Tuning for Graph Neural Networks
• CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
• NeurIPS 2024[Re] Reproducibility Study of “Explaining Temporal Graph Models Through an Explorer-Navigator Framework"
• ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
• ICLR 2024Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
• CVPR 2024FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer
• NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
• ICML 2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
• ICLR 2023$\mathscr{N}$-WL: A New Hierarchy of Expressivity for Graph Neural Networks
• CVPR 2023Adversarially Robust Neural Architecture Search for Graph Neural Networks
• NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
• ICML 2026Any-dimensional invariant universality
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Global-Aware Edge Prioritization for Pose Graph Initialization

All Graph Neural Networks papers

489 papers, page 4 of 10

151. NeurIPS 2024Almost Surely Asymptotically Constant Graph Neural Networks
152. NeurIPS 2024An End-To-End Graph Attention Network Hashing for Cross-Modal Retrieval
153. NeurIPS 2024Analysis of Corrected Graph Convolutions
154. NeurIPS 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learning
155. NeurIPS 2024Are Graph Neural Networks Optimal Approximation Algorithms?
156. NeurIPS 2024Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
157. NeurIPS 2024Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
158. NeurIPS 2024Boosting Graph Pooling with Persistent Homology
159. NeurIPS 2024Can Graph Learning Improve Planning in LLM-based Agents?
160. NeurIPS 2024Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach
161. NeurIPS 2024Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
162. NeurIPS 2024Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention
163. NeurIPS 2024Deep Graph Mating
164. NeurIPS 2024Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module
165. NeurIPS 2024Deep Homomorphism Networks
166. NeurIPS 2024DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
167. NeurIPS 2024DiGRAF: Diffeomorphic Graph-Adaptive Activation Function
168. NeurIPS 2024Distributed-Order Fractional Graph Operating Network
169. NeurIPS 2024DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
170. NeurIPS 2024Dynamic Rescaling for Training GNNs
171. NeurIPS 2024EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection
172. NeurIPS 2024Energy-based Epistemic Uncertainty for Graph Neural Networks
173. NeurIPS 2024Enhancing Chess Reinforcement Learning with Graph Representation
174. NeurIPS 2024Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
175. NeurIPS 2024Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
176. NeurIPS 2024Even Sparser Graph Transformers
177. NeurIPS 2024Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks
178. NeurIPS 2024Faster Local Solvers for Graph Diffusion Equations
179. NeurIPS 2024FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features
180. NeurIPS 2024Generalizing CNNs to graphs with learnable neighborhood quantization
181. NeurIPS 2024Geometric Trajectory Diffusion Models
182. NeurIPS 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Models
183. NeurIPS 2024Gradient Rewiring for Editable Graph Neural Network Training
184. NeurIPS 2024GRANOLA: Adaptive Normalization for Graph Neural Networks
185. NeurIPS 2024Graph Coarsening with Message-Passing Guarantees
186. NeurIPS 2024Graph Convolutions Enrich the Self-Attention in Transformers!
187. NeurIPS 2024Graph Neural Networks and Arithmetic Circuits
188. NeurIPS 2024Graph neural networks and non-commuting operators
189. NeurIPS 2024Graph Neural Networks Do Not Always Oversmooth
190. NeurIPS 2024Graph Neural Networks Need Cluster-Normalize-Activate Modules
191. NeurIPS 2024Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
192. NeurIPS 2024Graphcode: Learning from multiparameter persistent homology using graph neural networks
193. NeurIPS 2024GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction
194. NeurIPS 2024GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
195. NeurIPS 2024GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules
196. NeurIPS 2024HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
197. NeurIPS 2024HGDL: Heterogeneous Graph Label Distribution Learning
198. NeurIPS 2024If You Want to Be Robust, Be Wary of Initialization
199. NeurIPS 2024Improving Generalization of Dynamic Graph Learning via Environment Prompt
200. NeurIPS 2024Integrating GNN and Neural ODEs for Estimating Non-Reciprocal Two-Body Interactions in Mixed-Species Collective Motion

FAQ

How many Graph Neural Networks papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,038 in total: 434 in 2023, 208 in 2024, 213 in 2025, 183 in 2026.

How many Graph Neural Networks papers are at NeurIPS 2026?

NeurIPS 2026 has 74 Graph Neural Networks papers, versus 75 the year before.

How many Graph Neural Networks papers are at ICLR 2026?

ICLR 2026 has 28 Graph Neural Networks papers, versus 45 the year before.

How many Graph Neural Networks papers are at ICML 2026?

ICML 2026 has 68 Graph Neural Networks papers, versus 81 the year before.

How many Graph Neural Networks papers are at CVPR 2026?

CVPR 2026 has 13 Graph Neural Networks papers, versus 12 the year before.

Which conference publishes the most Graph Neural Networks papers?

NeurIPS, with 489 papers from 2023 to 2026.