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

489 papers, page 2 of 10

51. NeurIPS 2026Neural Networks With Dense Weights Are Not Universal Approximators
52. NeurIPS 2026Neutral-atom quantum features as complementary structural encodings for graph learning
53. NeurIPS 2026Normalized Architectures are Natively 4-Bit
54. NeurIPS 2026OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
55. NeurIPS 2026On Worst-Case Guarantees for Graph-Based Nearest-Neighbor Search
56. NeurIPS 2026Orlicz–Sobolev with Musielak: An Efficient Regularization Approach for Graph-based IPM
57. NeurIPS 2026PATH: A Dual Perspective for High-quality Text-attributed Graph Learning
58. NeurIPS 2026PhySPRING: Structure-Preserving Reduction of Physics-Informed Digital Twins via Graph Neural Networks
59. NeurIPS 2026Pointwise Lipschitz Continuous Graph Algorithms
60. NeurIPS 2026Practical Non-Stationary Graph Gaussian Processes
61. NeurIPS 2026Return to Basics: Very Simple Graph Contrastive Learning via Noise Cancellation Principle
62. NeurIPS 2026Robust Graph Diffusion Model
63. NeurIPS 2026Robust Surrogate Modeling for Explainable Graph Neural Networks
64. NeurIPS 2026Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
65. NeurIPS 2026Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting
66. NeurIPS 2026SpikeSTAG: A Dendritic Compartmental Spiking Graph Network for Multivariate Time-Series Forecasting
67. NeurIPS 2026SSDGExplainer: Structure-Semantic Dual-Guided Explainer for Graph Neural Networks
68. NeurIPS 2026Toward in Silico Strain Evaluation: A Multimodal Surrogate for Fermentation Dynamics with Metabolic Graph Pretraining
69. NeurIPS 2026Uncertainty-Aware Fuzzy Graph Contrastive Learning
70. NeurIPS 2026WavCIL: Wavelet Coefficient-Domain Invariant Learning for Dynamic Graph OOD Generalization
71. NeurIPS 2026Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them
72. NeurIPS 2026What to Perturb, How to Propagate: A Graph-Guided Transferable Attack on VLP Models
73. NeurIPS 2026When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors
74. NeurIPS 2026When Graph Structure Provably Helps Classification: Non-Asymptotic Recovery Guarantees
75. NeurIPS 2025$\texttt{STRCMP}$: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization
76. NeurIPS 2025A CLT for Polynomial GNNs on Community-Based Graphs
77. NeurIPS 2025Adjusted Count Quantification Learning on Graphs
78. NeurIPS 2025An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph
79. NeurIPS 2025Association-Focused Path Aggregation for Graph Fraud Detection
80. NeurIPS 2025Bridging Theory and Practice in Link Representation with Graph Neural Networks
81. NeurIPS 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework
82. NeurIPS 2025Depth-Width Tradeoffs for Transformers on Graph Tasks
83. NeurIPS 2025DiffLiG: Diffusion-enhanced Liquid Graph with Attention Propagation for Grid-to-Station Precipitation Correction
84. NeurIPS 2025Effects of Dropout on Performance in Long-range Graph Learning Tasks
85. NeurIPS 2025Enforcing convex constraints in Graph Neural Networks
86. NeurIPS 2025Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games
87. NeurIPS 2025Explore In-Context Message Passing Operator for Graph Neural Networks in A Mean Field Game
88. NeurIPS 2025FoGE: Fock Space inspired encoding for graph prompting
89. NeurIPS 2025GD$^2$: Robust Graph Learning under Label Noise via Dual-View Prediction Discrepancy
90. NeurIPS 2025Generative Graph Pattern Machine
91. NeurIPS 2025Geometric Logit Decoupling for Energy-Based Graph Out-of-distribution Detection
92. NeurIPS 2025Geometry-Aware Edge Pooling for Graph Neural Networks
93. NeurIPS 2025GnnXemplar: Exemplars to Explanations - Natural Language Rules for Global GNN Interpretability
94. NeurIPS 2025Graph Alignment via Birkhoff Relaxation
95. NeurIPS 2025Graph Data Selection for Domain Adaptation: A Model-Free Approach
96. NeurIPS 2025Graph Neural Network Based Action Ranking for Planning
97. NeurIPS 2025Graph Persistence goes Spectral
98. NeurIPS 2025Graph Your Own Prompt
99. NeurIPS 2025Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language Models
100. NeurIPS 2025GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining

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.