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 5 of 7

201. ICML 2024Position: Graph Foundation Models Are Already Here
202. ICML 2024Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
203. ICML 2024Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
204. ICML 2024Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE
205. ICML 2024Quantum Positional Encodings for Graph Neural Networks
206. ICML 2024Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
207. ICML 2024Robust Graph Matching when Nodes are Corrupt
208. ICML 2024S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning
209. ICML 2024Sign Rank Limitations for Inner Product Graph Decoders
210. ICML 2024SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter
211. ICML 2024Stay on Topic with Classifier-Free Guidance
212. ICML 2024Structure Your Data: Towards Semantic Graph Counterfactuals
213. ICML 2024Surprisingly Strong Performance Prediction with Neural Graph Features
214. ICML 2024The Expressive Power of Path-Based Graph Neural Networks
215. ICML 2024Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
216. ICML 2024Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
217. ICML 2024Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
218. ICML 2023A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
219. ICML 2023A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
220. ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
221. ICML 2023A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
222. ICML 2023Adaptive Smoothing Gradient Learning for Spiking Neural Networks
223. ICML 2023All in a Row: Compressed Convolution Networks for Graphs
224. ICML 2023Alternately Optimized Graph Neural Networks
225. ICML 2023Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels
226. ICML 2023Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process
227. ICML 2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
228. ICML 2023Boosting Graph Contrastive Learning via Graph Contrastive Saliency
229. ICML 2023Certified Robust Neural Networks: Generalization and Corruption Resistance
230. ICML 2023CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling
231. ICML 2023CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
232. ICML 2023Conditional Graph Information Bottleneck for Molecular Relational Learning
233. ICML 2023Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping
234. ICML 2023Conformal Prediction Sets for Graph Neural Networks
235. ICML 2023Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks
236. ICML 2023Disentangled Multiplex Graph Representation Learning
237. ICML 2023Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
238. ICML 2023Does a Neural Network Really Encode Symbolic Concepts?
239. ICML 2023ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
240. ICML 2023Effective Neural Topic Modeling with Embedding Clustering Regularization
241. ICML 2023Efficient Graph Field Integrators Meet Point Clouds
242. ICML 2023Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network
243. ICML 2023Efficient Parametric Approximations of Neural Network Function Space Distance
244. ICML 2023Efficiently predicting high resolution mass spectra with graph neural networks
245. ICML 2023Equivariant Polynomials for Graph Neural Networks
246. ICML 2023Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
247. ICML 2023Expectation-Complete Graph Representations with Homomorphisms
248. ICML 2023FAENet: Frame Averaging Equivariant GNN for Materials Modeling
249. ICML 2023Feature Expansion for Graph Neural Networks
250. ICML 2023Featured Graph Coarsening with Similarity Guarantees

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.