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

183 papers, page 1 of 4

1. CVPR 2026Anomaly as Non-Conformity via Training-Free Graph Laplacian Energy Minimization
2. CVPR 2026BrickNet Graph-Backed Generative Brick Assembly
3. CVPR 2026Dual Graph Regularized Deep Unfolding Network for Guided Depth Map
4. CVPR 2026FedSST Rethinking Fair Federated Graph Learning under Structural Shift
5. CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
6. CVPR 2026Global-Aware Edge Prioritization for Pose Graph Initialization
7. CVPR 2026GraPHFormer A Multimodal Graph Persistent Homology Transformer for the Analysis
8. CVPR 2026GSNR Graph Smooth Null-Space Representation for Inverse Problems
9. CVPR 2026Learning Scene Coordinate Reconstruction from Unposed Images via Pose Graph
10. CVPR 2026R2G A Multi-View Circuit Graph Benchmark Suite from RTL to
11. CVPR 2026Solvability of the Viewing Graph Under the Affine Camera Model
12. CVPR 2026Variational Graph-based Normal Integration
13. CVPR 2026Virtual Nodes Guided Dynamic Graph Neural Network for Brain Tumor
14. ICLR 2026A Graph Meta-Network for Learning on Kolmogorov–Arnold Networks
15. ICLR 2026AdS-GNN - a Conformally Equivariant Graph Neural Network
16. ICLR 2026Any-Subgroup Equivariant Networks via Symmetry Breaking
17. ICLR 2026Are we measuring oversmoothing in graph neural networks correctly?
18. ICLR 2026Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation
19. ICLR 2026Certified Evaluation of Model-Level Explanations for Graph Neural Networks
20. ICLR 2026DGNet: Learning Spatiotemporal PDEs with Discrete Green Networks
21. ICLR 2026Dynamic Kernel Graph Sparsifiers
22. ICLR 2026Fair Graph Machine Learning under Adversarial Missingness Processes
23. ICLR 2026Full-Graph vs. Mini-Batch Training: Comprehensive Analysis from a Batch Size and Fan-Out Size Perspective
24. ICLR 2026GGBall: Graph Generative Model on Poincaré Ball
25. ICLR 2026Glance for Context: Learning When to Leverage LLMs for Node-Aware GNN-LLM Fusion
26. ICLR 2026GNN Explanations that do not Explain and How to find Them
27. ICLR 2026GNN-as-Judge: Unleashing the Power of LLMs for Graph Few-shot Semi-supervised Learning with GNN Feedback
28. ICLR 2026Is Graph Unlearning Ready for Practice? A Benchmark on Efficiency, Utility, and Forgetting
29. ICLR 2026Laplacian Kernelized Bandit
30. ICLR 2026Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects
31. ICLR 2026Learning from Historical Activations in Graph Neural Networks
32. ICLR 2026Lightweight Transformer for EEG Classification via Balanced Signed Graph Algorithm Unrolling
33. ICLR 2026LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
34. ICLR 2026On The Expressive Power of GNN Derivatives
35. ICLR 2026One for Two: A Unified Framework for Imbalanced Graph Classification via Dynamic Balanced Prototype
36. ICLR 2026Out-of-Distribution Graph Models Merging
37. ICLR 2026Paradigm Shift of GNN Explainer from Label Space to Prototypical Representation Space
38. ICLR 2026Relational Graph Transformer
39. ICLR 2026Relatron: Automating Relational Machine Learning over Relational Databases
40. ICLR 2026TopoFormer: Topology Meets Attention for Graph Learning
41. ICLR 2026Weight Space Representation Learning on Diverse NeRF Architectures
42. ICML 2026A Unifying Relational Perspective on Expressive Lottery Tickets
43. ICML 2026Adaptive Node Feature Selection for Graph Neural Networks
44. ICML 2026Aitchison Embeddings for Learning Compositional Graph Representations
45. ICML 2026An Approximation Algorithm for Graph Label Selection
46. ICML 2026Anchor-guided Hypergraph Condensation with Dual-level Discrimination
47. ICML 2026Any-dimensional invariant universality
48. ICML 2026Backward Oversmoothing: why is it hard to train deep Graph Neural Networks?
49. ICML 2026Beyond Tokens: Enhancing RTL Quality Estimation via Structural Graph Learning
50. ICML 2026Bipartite Graph Attention-based Clustering for Large-scale scRNA-seq 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.