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 8 of 9

351. NeurIPS 2023NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
352. NeurIPS 2023Newton–Cotes Graph Neural Networks: On the Time Evolution of Dynamic Systems
353. NeurIPS 2023No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change Maximization for Active Learning
354. NeurIPS 2023Norm-based Generalization Bounds for Sparse Neural Networks
355. NeurIPS 2023Normalization-Equivariant Neural Networks with Application to Image Denoising
356. NeurIPS 2023Normalizing flow neural networks by JKO scheme
357. NeurIPS 2023On the Ability of Graph Neural Networks to Model Interactions Between Vertices
358. NeurIPS 2023On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
359. NeurIPS 2023On the spectral bias of two-layer linear networks
360. NeurIPS 2023OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
361. NeurIPS 2023Optimality of Message-Passing Architectures for Sparse Graphs
362. NeurIPS 2023Optimization and Bayes: A Trade-off for Overparameterized Neural Networks
363. NeurIPS 2023Optimizing over trained GNNs via symmetry breaking
364. NeurIPS 2023Penalising the biases in norm regularisation enforces sparsity
365. NeurIPS 2023PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis
366. NeurIPS 2023Permutation Equivariant Neural Functionals
367. NeurIPS 2023Polynomially Over-Parameterized Convolutional Neural Networks Contain Structured Strong Winning Lottery Tickets
368. NeurIPS 2023Predicting Global Label Relationship Matrix for Graph Neural Networks under Heterophily
369. NeurIPS 2023Probabilistic Weight Fixing: Large-scale training of neural network weight uncertainties for quantisation.
370. NeurIPS 2023Provable Guarantees for Neural Networks via Gradient Feature Learning
371. NeurIPS 2023Provable Training for Graph Contrastive Learning
372. NeurIPS 2023QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
373. NeurIPS 2023Quasi-Monte Carlo Graph Random Features
374. NeurIPS 2023Randomized Sparse Neural Galerkin Schemes for Solving Evolution Equations with Deep Networks
375. NeurIPS 2023Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
376. NeurIPS 2023Recurrent Temporal Revision Graph Networks
377. NeurIPS 2023Red Teaming Deep Neural Networks with Feature Synthesis Tools
378. NeurIPS 2023Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
379. NeurIPS 2023Rethinking the Backward Propagation for Adversarial Transferability
380. NeurIPS 2023Reversible and irreversible bracket-based dynamics for deep graph neural networks
381. NeurIPS 2023Riemannian Laplace approximations for Bayesian neural networks
382. NeurIPS 2023Riemannian Residual Neural Networks
383. NeurIPS 2023Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model
384. NeurIPS 2023Robustness Guarantees for Adversarially Trained Neural Networks
385. NeurIPS 2023Self-supervised Graph Neural Networks via Low-Rank Decomposition
386. NeurIPS 2023Separable Physics-Informed Neural Networks
387. NeurIPS 2023SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
388. NeurIPS 2023Sheaf Hypergraph Networks
389. NeurIPS 2023Should Under-parameterized Student Networks Copy or Average Teacher Weights?
390. NeurIPS 2023Simplicity Bias in 1-Hidden Layer Neural Networks
391. NeurIPS 2023Simplifying Neural Network Training Under Class Imbalance
392. NeurIPS 2023Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization
393. NeurIPS 2023Spectral Evolution and Invariance in Linear-width Neural Networks
394. NeurIPS 2023Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
395. NeurIPS 2023Spike-driven Transformer
396. NeurIPS 2023Spiking PointNet: Spiking Neural Networks for Point Clouds
397. NeurIPS 2023Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
398. NeurIPS 2023Stochastic Approximation Algorithms for Systems of Interacting Particles
399. NeurIPS 2023Structure of universal formulas
400. NeurIPS 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data

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.