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 7 of 10

301. NeurIPS 2023Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
302. NeurIPS 2023Efficient Learning of Linear Graph Neural Networks via Node Subsampling
303. NeurIPS 2023Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
304. NeurIPS 2023Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity
305. NeurIPS 2023Empowering Convolutional Neural Nets with MetaSin Activation
306. NeurIPS 2023Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
307. NeurIPS 2023Enhancing Sharpness-Aware Optimization Through Variance Suppression
308. NeurIPS 2023Entropic Neural Optimal Transport via Diffusion Processes
309. NeurIPS 2023Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs
310. NeurIPS 2023Equivariant Neural Operator Learning with Graphon Convolution
311. NeurIPS 2023Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
312. NeurIPS 2023Euler-Lagrange Analysis of Generative Adversarial Networks
313. NeurIPS 2023Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
314. NeurIPS 2023Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis
315. NeurIPS 2023Evolving Connectivity for Recurrent Spiking Neural Networks
316. NeurIPS 2023Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
317. NeurIPS 2023Exact Verification of ReLU Neural Control Barrier Functions
318. NeurIPS 2023Explainable Brain Age Prediction using coVariance Neural Networks
319. NeurIPS 2023Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
320. NeurIPS 2023Expressive probabilistic sampling in recurrent neural networks
321. NeurIPS 2023Expressive Sign Equivariant Networks for Spectral Geometric Learning
322. NeurIPS 2023Expressivity-Preserving GNN Simulation
323. NeurIPS 2023Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
324. NeurIPS 2023Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
325. NeurIPS 2023Fair Graph Distillation
326. NeurIPS 2023FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
327. NeurIPS 2023Fine-grained Expressivity of Graph Neural Networks
328. NeurIPS 2023Flat Seeking Bayesian Neural Networks
329. NeurIPS 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
330. NeurIPS 2023From Tempered to Benign Overfitting in ReLU Neural Networks
331. NeurIPS 2023Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
332. NeurIPS 2023Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
333. NeurIPS 2023Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
334. NeurIPS 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
335. NeurIPS 2023Generalised f-Mean Aggregation for Graph Neural Networks
336. NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
337. NeurIPS 2023GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
338. NeurIPS 2023GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
339. NeurIPS 2023Going beyond persistent homology using persistent homology
340. NeurIPS 2023GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
341. NeurIPS 2023Graph Clustering with Graph Neural Networks
342. NeurIPS 2023Graph Convolutional Kernel Machine versus Graph Convolutional Networks
343. NeurIPS 2023Graph Denoising Diffusion for Inverse Protein Folding
344. NeurIPS 2023Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
345. NeurIPS 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis
346. NeurIPS 2023Graph of Circuits with GNN for Exploring the Optimal Design Space
347. NeurIPS 2023GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search
348. NeurIPS 2023GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
349. NeurIPS 2023GSLB: The Graph Structure Learning Benchmark
350. NeurIPS 2023Hierarchical Randomized Smoothing

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.