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

213 papers, page 4 of 5

151. NeurIPS 2025Explore In-Context Message Passing Operator for Graph Neural Networks in A Mean Field Game
152. NeurIPS 2025FoGE: Fock Space inspired encoding for graph prompting
153. NeurIPS 2025GD$^2$: Robust Graph Learning under Label Noise via Dual-View Prediction Discrepancy
154. NeurIPS 2025Generative Graph Pattern Machine
155. NeurIPS 2025Geometric Logit Decoupling for Energy-Based Graph Out-of-distribution Detection
156. NeurIPS 2025Geometry-Aware Edge Pooling for Graph Neural Networks
157. NeurIPS 2025GnnXemplar: Exemplars to Explanations - Natural Language Rules for Global GNN Interpretability
158. NeurIPS 2025Graph Alignment via Birkhoff Relaxation
159. NeurIPS 2025Graph Data Selection for Domain Adaptation: A Model-Free Approach
160. NeurIPS 2025Graph Neural Network Based Action Ranking for Planning
161. NeurIPS 2025Graph Persistence goes Spectral
162. NeurIPS 2025Graph Your Own Prompt
163. NeurIPS 2025Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language Models
164. NeurIPS 2025GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining
165. NeurIPS 2025GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data
166. NeurIPS 2025GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
167. NeurIPS 2025HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations
168. NeurIPS 2025Hierarchical Shortest-Path Graph Kernel Network
169. NeurIPS 2025Higher-Order Learning with Graph Neural Networks via Hypergraph Encodings
170. NeurIPS 2025How Does Topology Bias Distort Message Passing in Graph Recommender? A Dirichlet Energy Perspective
171. NeurIPS 2025HubGT: Fast Graph Transformer with Decoupled Hierarchy Labeling
172. NeurIPS 2025Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
173. NeurIPS 2025Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models
174. NeurIPS 2025Local-Global Coupling Spiking Graph Transformer for Brain Disorders Diagnosis from Two Perspectives
175. NeurIPS 2025Logical Expressiveness of Graph Neural Networks with Hierarchical Node Individualization
176. NeurIPS 2025Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness–Generalization Perspective
177. NeurIPS 2025MaNGO — Adaptable Graph Network Simulators via Meta-Learning
178. NeurIPS 2025Memorization in Graph Neural Networks
179. NeurIPS 2025Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow Variants
180. NeurIPS 2025NeuroH-TGL: Neuro-Heterogeneity Guided Temporal Graph Learning Strategy for Brain Disease Diagnosis
181. NeurIPS 2025On Logic-based Self-Explainable Graph Neural Networks
182. NeurIPS 2025On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
183. NeurIPS 2025On topological descriptors for graph products
184. NeurIPS 2025Optimal Graph Clustering without Edge Density Signals
185. NeurIPS 2025Over-squashing in Spatiotemporal Graph Neural Networks
186. NeurIPS 2025ProfiX: Improving Profile-Guided Optimization in Compilers with Graph Neural Networks
187. NeurIPS 2025Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization
188. NeurIPS 2025Pseudo-Riemannian Graph Transformer
189. NeurIPS 2025RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases
190. NeurIPS 2025Refining Norms: A Post-hoc Framework for OOD Detection in Graph Neural Networks
191. NeurIPS 2025Relieving the Over-Aggregating Effect in Graph Transformers
192. NeurIPS 2025Rethinking Tokenized Graph Transformers for Node Classification
193. NeurIPS 2025Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks
194. NeurIPS 2025Revisiting 1-peer exponential graph for enhancing decentralized learning efficiency
195. NeurIPS 2025Revolutionizing Graph Aggregation: From Suppression to Amplification via BoostGCN
196. NeurIPS 2025Robust Explanations of Graph Neural Networks via Graph Curvatures
197. NeurIPS 2025Robust Graph Condensation via Classification Complexity Mitigation
198. NeurIPS 2025Self-Assembling Graph Perceptrons
199. NeurIPS 2025SignFlow Bipartite Subgraph Network For Large-Scale Graph Link Sign Prediction
200. NeurIPS 2025SONAR: Long-Range Graph Propagation Through Information Waves

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.