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

208 papers, page 4 of 5

151. NeurIPS 2024Geometric Trajectory Diffusion Models
152. NeurIPS 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Models
153. NeurIPS 2024Gradient Rewiring for Editable Graph Neural Network Training
154. NeurIPS 2024GRANOLA: Adaptive Normalization for Graph Neural Networks
155. NeurIPS 2024Graph Coarsening with Message-Passing Guarantees
156. NeurIPS 2024Graph Convolutions Enrich the Self-Attention in Transformers!
157. NeurIPS 2024Graph Neural Networks and Arithmetic Circuits
158. NeurIPS 2024Graph neural networks and non-commuting operators
159. NeurIPS 2024Graph Neural Networks Do Not Always Oversmooth
160. NeurIPS 2024Graph Neural Networks Need Cluster-Normalize-Activate Modules
161. NeurIPS 2024Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
162. NeurIPS 2024Graphcode: Learning from multiparameter persistent homology using graph neural networks
163. NeurIPS 2024GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction
164. NeurIPS 2024GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
165. NeurIPS 2024GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules
166. NeurIPS 2024HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
167. NeurIPS 2024HGDL: Heterogeneous Graph Label Distribution Learning
168. NeurIPS 2024If You Want to Be Robust, Be Wary of Initialization
169. NeurIPS 2024Improving Generalization of Dynamic Graph Learning via Environment Prompt
170. NeurIPS 2024Integrating GNN and Neural ODEs for Estimating Non-Reciprocal Two-Body Interactions in Mixed-Species Collective Motion
171. NeurIPS 2024Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
172. NeurIPS 2024Iterative Methods via Locally Evolving Set Process
173. NeurIPS 2024Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrieval
174. NeurIPS 2024Knowledge Graph Completion by Intermediate Variables Regularization
175. NeurIPS 2024Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
176. NeurIPS 2024LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
177. NeurIPS 2024Long-range Brain Graph Transformer
178. NeurIPS 2024NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
179. NeurIPS 2024Non-convolutional graph neural networks.
180. NeurIPS 2024On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
181. NeurIPS 2024On the Power of Small-size Graph Neural Networks for Linear Programming
182. NeurIPS 2024PowerGraph: A power grid benchmark dataset for graph neural networks
183. NeurIPS 2024Probabilistic Graph Rewiring via Virtual Nodes
184. NeurIPS 2024ProG: A Graph Prompt Learning Benchmark
185. NeurIPS 2024RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
186. NeurIPS 2024RelBench: A Benchmark for Deep Learning on Relational Databases
187. NeurIPS 2024Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations
188. NeurIPS 2024Rethinking the Capacity of Graph Neural Networks for Branching Strategy
189. NeurIPS 2024Revisiting Score Propagation in Graph Out-of-Distribution Detection
190. NeurIPS 2024Robust Graph Neural Networks via Unbiased Aggregation
191. NeurIPS 2024Scale Equivariant Graph Metanetworks
192. NeurIPS 2024Schur Nets: exploiting local structure for equivariance in higher order graph neural networks
193. NeurIPS 2024Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks
194. NeurIPS 2024Similarity-Navigated Conformal Prediction for Graph Neural Networks
195. NeurIPS 2024Spatio-Spectral Graph Neural Networks
196. NeurIPS 2024Spiking Graph Neural Network on Riemannian Manifolds
197. NeurIPS 2024Stochastic contextual bandits with graph feedback: from independence number to MAS number
198. NeurIPS 2024Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed
199. NeurIPS 2024The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks
200. NeurIPS 2024Towards Dynamic Message Passing on Graphs

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.