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

313 papers, page 4 of 7

151. ICML 2024A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering
152. ICML 2024An Efficient Maximal Ancestral Graph Listing Algorithm
153. ICML 2024An Empirical Study of Realized GNN Expressiveness
154. ICML 2024Class-Imbalanced Graph Learning without Class Rebalancing
155. ICML 2024Collective Certified Robustness against Graph Injection Attacks
156. ICML 2024Community-Invariant Graph Contrastive Learning
157. ICML 2024Comparing Graph Transformers via Positional Encodings
158. ICML 2024Cooperative Graph Neural Networks
159. ICML 2024Delaunay Graph: Addressing Over-Squashing and Over-Smoothing Using Delaunay Triangulation
160. ICML 2024Disentangled Continual Graph Neural Architecture Search with Invariant Modular Supernet
161. ICML 2024Editing Partially Observable Networks via Graph Diffusion Models
162. ICML 2024Effective Federated Graph Matching
163. ICML 2024Empowering Graph Invariance Learning with Deep Spurious Infomax
164. ICML 2024EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
165. ICML 2024Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective
166. ICML 2024Generalization Error of Graph Neural Networks in the Mean-field Regime
167. ICML 2024Generalized Sobolev Transport for Probability Measures on a Graph
168. ICML 2024Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
169. ICML 2024Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
170. ICML 2024Graph As Point Set
171. ICML 2024Graph Automorphism Group Equivariant Neural Networks
172. ICML 2024Graph Distillation with Eigenbasis Matching
173. ICML 2024Graph External Attention Enhanced Transformer
174. ICML 2024Graph Geometry-Preserving Autoencoders
175. ICML 2024Graph Mixup on Approximate Gromov–Wasserstein Geodesics
176. ICML 2024Graph Neural Network Explanations are Fragile
177. ICML 2024Graph Neural Networks Use Graphs When They Shouldn't
178. ICML 2024Graph Neural Networks with a Distribution of Parametrized Graphs
179. ICML 2024Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification
180. ICML 2024Graph Structure Extrapolation for Out-of-Distribution Generalization
181. ICML 2024Graph-Triggered Rising Bandits
182. ICML 2024HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
183. ICML 2024Homomorphism Counts for Graph Neural Networks: All About That Basis
184. ICML 2024How Graph Neural Networks Learn: Lessons from Training Dynamics
185. ICML 2024How Interpretable Are Interpretable Graph Neural Networks?
186. ICML 2024How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing
187. ICML 2024Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning
188. ICML 2024Learning Divergence Fields for Shift-Robust Graph Representations
189. ICML 2024Learning Graph Representation via Graph Entropy Maximization
190. ICML 2024Learning Latent Structures in Network Games via Data-Dependent Gated-Prior Graph Variational Autoencoders
191. ICML 2024Less is More: on the Over-Globalizing Problem in Graph Transformers
192. ICML 2024LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering
193. ICML 2024Modelling Microbial Communities with Graph Neural Networks
194. ICML 2024Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
195. ICML 2024Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
196. ICML 2024On the Expressive Power of Spectral Invariant Graph Neural Networks
197. ICML 2024On the Generalization of Equivariant Graph Neural Networks
198. ICML 2024On the Role of Edge Dependency in Graph Generative Models
199. ICML 2024PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
200. ICML 2024Perfect Alignment May be Poisonous to Graph Contrastive Learning

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.