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

434 papers, page 4 of 9

151. ICML 2023MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
152. ICML 2023Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
153. ICML 2023Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
154. ICML 2023Moccasin: Efficient Tensor Rematerialization for Neural Networks
155. ICML 2023Multi-class Graph Clustering via Approximated Effective $p$-Resistance
156. ICML 2023Neural networks trained with SGD learn distributions of increasing complexity
157. ICML 2023Neural signature kernels as infinite-width-depth-limits of controlled ResNets
158. ICML 2023Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks
159. ICML 2023Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
160. ICML 2023On the Expressive Power of Geometric Graph Neural Networks
161. ICML 2023On the Initialization of Graph Neural Networks
162. ICML 2023Path Neural Networks: Expressive and Accurate Graph Neural Networks
163. ICML 2023Perturbation Analysis of Neural Collapse
164. ICML 2023Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
165. ICML 2023Randomized Schur Complement Views for Graph Contrastive Learning
166. ICML 2023Relevant Walk Search for Explaining Graph Neural Networks
167. ICML 2023Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
168. ICML 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networks
169. ICML 2023RGE: A Repulsive Graph Rectification for Node Classification via Influence
170. ICML 2023RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
171. ICML 2023SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
172. ICML 2023SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
173. ICML 2023Statistical Foundations of Prior-Data Fitted Networks
174. ICML 2023Taming graph kernels with random features
175. ICML 2023The Acquisition of Physical Knowledge in Generative Neural Networks
176. ICML 2023The Hessian perspective into the Nature of Convolutional Neural Networks
177. ICML 2023Total Variation Graph Neural Networks
178. ICML 2023Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
179. ICML 2023Towards Deep Attention in Graph Neural Networks: Problems and Remedies
180. ICML 2023Towards Understanding Generalization of Graph Neural Networks
181. ICML 2023Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data
182. ICML 2023Vertical Federated Graph Neural Network for Recommender System
183. ICML 2023XAI Beyond Classification: Interpretable Neural Clustering
184. NeurIPS 2023[Re] $\mathcal{G}$-Mixup: Graph Data Augmentation for Graph Classification
185. NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
186. NeurIPS 2023[Re] Variational Neural Cellular Automata
187. NeurIPS 2023A Fractional Graph Laplacian Approach to Oversmoothing
188. NeurIPS 2023A graphon-signal analysis of graph neural networks
189. NeurIPS 2023A Metadata-Driven Approach to Understand Graph Neural Networks
190. NeurIPS 2023A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
191. NeurIPS 2023A new perspective on building efficient and expressive 3D equivariant graph neural networks
192. NeurIPS 2023A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation
193. NeurIPS 2023A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time
194. NeurIPS 2023A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
195. NeurIPS 2023Accelerating Molecular Graph Neural Networks via Knowledge Distillation
196. NeurIPS 2023Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
197. NeurIPS 2023Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
198. NeurIPS 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
199. NeurIPS 2023Affinity-Aware Graph Networks
200. NeurIPS 2023AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity

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.