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

96 papers, page 2 of 2

51. ICML 2023Graph Positional Encoding via Random Feature Propagation
52. ICML 2023Graph Switching Dynamical Systems
53. ICML 2023GREAD: Graph Neural Reaction-Diffusion Networks
54. ICML 2023Half-Hop: A graph upsampling approach for slowing down message passing
55. ICML 2023HOPE: High-order Graph ODE For Modeling Interacting Dynamics
56. ICML 2023Implicit Graph Neural Networks: A Monotone Operator Viewpoint
57. ICML 2023Improving Graph Neural Networks with Learnable Propagation Operators
58. ICML 2023InGram: Inductive Knowledge Graph Embedding via Relation Graphs
59. ICML 2023LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
60. ICML 2023Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
61. ICML 2023LinSATNet: The Positive Linear Satisfiability Neural Networks
62. ICML 2023Local Vertex Colouring Graph Neural Networks
63. ICML 2023Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks
64. ICML 2023MG-GNN: Multigrid Graph Neural Networks for Learning Multilevel Domain Decomposition Methods
65. ICML 2023Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
66. ICML 2023Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
67. ICML 2023Moccasin: Efficient Tensor Rematerialization for Neural Networks
68. ICML 2023Multi-class Graph Clustering via Approximated Effective $p$-Resistance
69. ICML 2023Neural networks trained with SGD learn distributions of increasing complexity
70. ICML 2023Neural signature kernels as infinite-width-depth-limits of controlled ResNets
71. ICML 2023Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks
72. ICML 2023Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
73. ICML 2023On the Expressive Power of Geometric Graph Neural Networks
74. ICML 2023On the Initialization of Graph Neural Networks
75. ICML 2023Path Neural Networks: Expressive and Accurate Graph Neural Networks
76. ICML 2023Perturbation Analysis of Neural Collapse
77. ICML 2023Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
78. ICML 2023Randomized Schur Complement Views for Graph Contrastive Learning
79. ICML 2023Relevant Walk Search for Explaining Graph Neural Networks
80. ICML 2023Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
81. ICML 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networks
82. ICML 2023RGE: A Repulsive Graph Rectification for Node Classification via Influence
83. ICML 2023RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
84. ICML 2023SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
85. ICML 2023SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
86. ICML 2023Statistical Foundations of Prior-Data Fitted Networks
87. ICML 2023Taming graph kernels with random features
88. ICML 2023The Acquisition of Physical Knowledge in Generative Neural Networks
89. ICML 2023The Hessian perspective into the Nature of Convolutional Neural Networks
90. ICML 2023Total Variation Graph Neural Networks
91. ICML 2023Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
92. ICML 2023Towards Deep Attention in Graph Neural Networks: Problems and Remedies
93. ICML 2023Towards Understanding Generalization of Graph Neural Networks
94. ICML 2023Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data
95. ICML 2023Vertical Federated Graph Neural Network for Recommender System
96. ICML 2023XAI Beyond Classification: Interpretable Neural Clustering

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.