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

1,038 papers, page 21 of 21

1001. NeurIPS 2023Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
1002. NeurIPS 2023Stochastic Approximation Algorithms for Systems of Interacting Particles
1003. NeurIPS 2023Structure of universal formulas
1004. NeurIPS 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
1005. NeurIPS 2023SyncTREE: Fast Timing Analysis for Integrated Circuit Design through a Physics-informed Tree-based Graph Neural Network
1006. NeurIPS 2023Tailoring Self-Attention for Graph via Rooted Subtrees
1007. NeurIPS 2023Taming Local Effects in Graph-based Spatiotemporal Forecasting
1008. NeurIPS 2023TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
1009. NeurIPS 2023The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
1010. NeurIPS 2023The expressive power of pooling in Graph Neural Networks
1011. NeurIPS 2023The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models
1012. NeurIPS 2023The Separation Capacity of Random Neural Networks
1013. NeurIPS 2023Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
1014. NeurIPS 2023Three Iterations of (d − 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points
1015. NeurIPS 2023Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
1016. NeurIPS 2023Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach
1017. NeurIPS 2023Towards Label Position Bias in Graph Neural Networks
1018. NeurIPS 2023Towards Self-Interpretable Graph-Level Anomaly Detection
1019. NeurIPS 2023Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks
1020. NeurIPS 2023Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies
1021. NeurIPS 2023Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
1022. NeurIPS 2023Training Neural Networks is NP-Hard in Fixed Dimension
1023. NeurIPS 2023Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?
1024. NeurIPS 2023Transformers over Directed Acyclic Graphs
1025. NeurIPS 2023Trial matching: capturing variability with data-constrained spiking neural networks
1026. NeurIPS 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
1027. NeurIPS 2023Universal Prompt Tuning for Graph Neural Networks
1028. NeurIPS 2023Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
1029. NeurIPS 2023Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision
1030. NeurIPS 2023Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation
1031. NeurIPS 2023V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs
1032. NeurIPS 2023Variational Annealing on Graphs for Combinatorial Optimization
1033. NeurIPS 2023Wasserstein distributional robustness of neural networks
1034. NeurIPS 2023What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
1035. NeurIPS 2023What Planning Problems Can A Relational Neural Network Solve?
1036. NeurIPS 2023When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability
1037. NeurIPS 2023Window-Based Distribution Shift Detection for Deep Neural Networks
1038. NeurIPS 2023Zero-One Laws of Graph Neural Networks

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.