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 15 of 21

701. ICML 2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
702. ICML 2023Boosting Graph Contrastive Learning via Graph Contrastive Saliency
703. ICML 2023Certified Robust Neural Networks: Generalization and Corruption Resistance
704. ICML 2023CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling
705. ICML 2023CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
706. ICML 2023Conditional Graph Information Bottleneck for Molecular Relational Learning
707. ICML 2023Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping
708. ICML 2023Conformal Prediction Sets for Graph Neural Networks
709. ICML 2023Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks
710. ICML 2023Disentangled Multiplex Graph Representation Learning
711. ICML 2023Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
712. ICML 2023Does a Neural Network Really Encode Symbolic Concepts?
713. ICML 2023ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
714. ICML 2023Effective Neural Topic Modeling with Embedding Clustering Regularization
715. ICML 2023Efficient Graph Field Integrators Meet Point Clouds
716. ICML 2023Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network
717. ICML 2023Efficient Parametric Approximations of Neural Network Function Space Distance
718. ICML 2023Efficiently predicting high resolution mass spectra with graph neural networks
719. ICML 2023Equivariant Polynomials for Graph Neural Networks
720. ICML 2023Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
721. ICML 2023Expectation-Complete Graph Representations with Homomorphisms
722. ICML 2023FAENet: Frame Averaging Equivariant GNN for Materials Modeling
723. ICML 2023Feature Expansion for Graph Neural Networks
724. ICML 2023Featured Graph Coarsening with Similarity Guarantees
725. ICML 2023From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
726. ICML 2023Function-Space Regularization in Neural Networks: A Probabilistic Perspective
727. ICML 2023GC-Flow: A Graph-Based Flow Network for Effective Clustering
728. ICML 2023Generalizing Neural Wave Functions
729. ICML 2023Generated Graph Detection
730. ICML 2023Generative Graph Dictionary Learning
731. ICML 2023Geometric Clifford Algebra Networks
732. ICML 2023GFlowOut: Dropout with Generative Flow Networks
733. ICML 2023GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming
734. ICML 2023Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks
735. ICML 2023Graph Generative Model for Benchmarking Graph Neural Networks
736. ICML 2023Graph Inductive Biases in Transformers without Message Passing
737. ICML 2023Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
738. ICML 2023Graph Mixup with Soft Alignments
739. ICML 2023Graph Neural Networks can Recover the Hidden Features Solely from the Graph Structure
740. ICML 2023Graph Neural Networks with Learnable and Optimal Polynomial Bases
741. ICML 2023Graph Neural Tangent Kernel: Convergence on Large Graphs
742. ICML 2023Graph Positional Encoding via Random Feature Propagation
743. ICML 2023Graph Switching Dynamical Systems
744. ICML 2023GREAD: Graph Neural Reaction-Diffusion Networks
745. ICML 2023Half-Hop: A graph upsampling approach for slowing down message passing
746. ICML 2023HOPE: High-order Graph ODE For Modeling Interacting Dynamics
747. ICML 2023Implicit Graph Neural Networks: A Monotone Operator Viewpoint
748. ICML 2023Improving Graph Neural Networks with Learnable Propagation Operators
749. ICML 2023InGram: Inductive Knowledge Graph Embedding via Relation Graphs
750. ICML 2023LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation

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.