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

489 papers, page 5 of 10

201. NeurIPS 2024Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
202. NeurIPS 2024Iterative Methods via Locally Evolving Set Process
203. NeurIPS 2024Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrieval
204. NeurIPS 2024Knowledge Graph Completion by Intermediate Variables Regularization
205. NeurIPS 2024Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
206. NeurIPS 2024LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
207. NeurIPS 2024Long-range Brain Graph Transformer
208. NeurIPS 2024NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
209. NeurIPS 2024Non-convolutional graph neural networks.
210. NeurIPS 2024On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
211. NeurIPS 2024On the Power of Small-size Graph Neural Networks for Linear Programming
212. NeurIPS 2024PowerGraph: A power grid benchmark dataset for graph neural networks
213. NeurIPS 2024Probabilistic Graph Rewiring via Virtual Nodes
214. NeurIPS 2024ProG: A Graph Prompt Learning Benchmark
215. NeurIPS 2024RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
216. NeurIPS 2024RelBench: A Benchmark for Deep Learning on Relational Databases
217. NeurIPS 2024Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations
218. NeurIPS 2024Rethinking the Capacity of Graph Neural Networks for Branching Strategy
219. NeurIPS 2024Revisiting Score Propagation in Graph Out-of-Distribution Detection
220. NeurIPS 2024Robust Graph Neural Networks via Unbiased Aggregation
221. NeurIPS 2024Scale Equivariant Graph Metanetworks
222. NeurIPS 2024Schur Nets: exploiting local structure for equivariance in higher order graph neural networks
223. NeurIPS 2024Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks
224. NeurIPS 2024Similarity-Navigated Conformal Prediction for Graph Neural Networks
225. NeurIPS 2024Spatio-Spectral Graph Neural Networks
226. NeurIPS 2024Spiking Graph Neural Network on Riemannian Manifolds
227. NeurIPS 2024Stochastic contextual bandits with graph feedback: from independence number to MAS number
228. NeurIPS 2024Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed
229. NeurIPS 2024The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks
230. NeurIPS 2024Towards Dynamic Message Passing on Graphs
231. NeurIPS 2024Towards Principled Graph Transformers
232. NeurIPS 2024UGC: Universal Graph Coarsening
233. NeurIPS 2024Understanding Transformer Reasoning Capabilities via Graph Algorithms
234. NeurIPS 2024Unified Graph Augmentations for Generalized Contrastive Learning on Graphs
235. NeurIPS 2024What do Graph Neural Networks learn? Insights from Tropical Geometry
236. NeurIPS 2024What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks
237. NeurIPS 2024What Matters in Graph Class Incremental Learning? An Information Preservation Perspective
238. NeurIPS 2024You Only Cache Once: Decoder-Decoder Architectures for Language Models
239. NeurIPS 2023[Re] $\mathcal{G}$-Mixup: Graph Data Augmentation for Graph Classification
240. NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
241. NeurIPS 2023[Re] Variational Neural Cellular Automata
242. NeurIPS 2023A Fractional Graph Laplacian Approach to Oversmoothing
243. NeurIPS 2023A graphon-signal analysis of graph neural networks
244. NeurIPS 2023A Metadata-Driven Approach to Understand Graph Neural Networks
245. NeurIPS 2023A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
246. NeurIPS 2023A new perspective on building efficient and expressive 3D equivariant graph neural networks
247. NeurIPS 2023A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation
248. NeurIPS 2023A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time
249. NeurIPS 2023A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
250. NeurIPS 2023Accelerating Molecular Graph Neural Networks via Knowledge Distillation

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.