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

187 papers, page 2 of 4

51. ICLR 2025Learning Equivariant Non-Local Electron Density Functionals
52. ICLR 2025Learning Graph Invariance by Harnessing Spuriosity
53. ICLR 2025Learning Graph Quantized Tokenizers
54. ICLR 2025Learning Long Range Dependencies on Graphs via Random Walks
55. ICLR 2025Learning Partial Graph Matching via Optimal Partial Transport
56. ICLR 2025Let Your Features Tell The Differences: Understanding Graph Convolution By Feature Splitting
57. ICLR 2025MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy
58. ICLR 2025Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
59. ICLR 2025On the Completeness of Invariant Geometric Deep Learning Models
60. ICLR 2025On the Hölder Stability of Multiset and Graph Neural Networks
61. ICLR 2025PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems
62. ICLR 2025Precedence-Constrained Winter Value for Effective Graph Data Valuation
63. ICLR 2025Rethinking Graph Neural Networks From A Geometric Perspective Of Node Features
64. ICLR 2025Scale-Free Graph-Language Models
65. ICLR 2025Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation
66. ICLR 2025SINGER: Stochastic Network Graph Evolving Operator for High Dimensional PDEs
67. ICLR 2025Spectro-Riemannian Graph Neural Networks
68. ICLR 2025Towards a Complete Logical Framework for GNN Expressiveness
69. ICLR 2025Towards Bridging Generalization and Expressivity of Graph Neural Networks
70. ICLR 2025Towards Explaining the Power of Constant-depth Graph Neural Networks for Structured Linear Programming
71. ICLR 2025Valid Conformal Prediction for Dynamic GNNs
72. ICLR 2025When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach
73. ICLR 2025When Graph Neural Networks Meet Dynamic Mode Decomposition
74. ICLR 2024A Topological Perspective on Demystifying GNN-Based Link Prediction Performance
75. ICLR 2024Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
76. ICLR 2024Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
77. ICLR 2024Clifford Group Equivariant Simplicial Message Passing Networks
78. ICLR 2024Conformal Inductive Graph Neural Networks
79. ICLR 2024CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs
80. ICLR 2024Counting Graph Substructures with Graph Neural Networks
81. ICLR 2024Effective Structural Encodings via Local Curvature Profiles
82. ICLR 2024Equivariant Matrix Function Neural Networks
83. ICLR 2024EX-Graph: A Pioneering Dataset Bridging Ethereum and X
84. ICLR 2024Forward Learning of Graph Neural Networks
85. ICLR 2024From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
86. ICLR 2024G$^2$N$^2$ : Weisfeiler and Lehman go grammatical
87. ICLR 2024GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking
88. ICLR 2024GOAt: Explaining Graph Neural Networks via Graph Output Attribution
89. ICLR 2024Graph Lottery Ticket Automated
90. ICLR 2024Graph Metanetworks for Processing Diverse Neural Architectures
91. ICLR 2024Graph Neural Networks for Learning Equivariant Representations of Neural Networks
92. ICLR 2024Graph Parsing Networks
93. ICLR 2024GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks
94. ICLR 2024Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning
95. ICLR 2024HiGen: Hierarchical Graph Generative Networks
96. ICLR 2024Hybrid Directional Graph Neural Network for Molecules
97. ICLR 2024Mirage: Model-agnostic Graph Distillation for Graph Classification
98. ICLR 2024MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy
99. ICLR 2024Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
100. ICLR 2024NeuroBack: Improving CDCL SAT Solving using 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.