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

251 papers, page 2 of 6

51. NeurIPS 2023Demystifying Oversmoothing in Attention-Based Graph Neural Networks
52. NeurIPS 2023Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
53. NeurIPS 2023Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
54. NeurIPS 2023Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection
55. NeurIPS 2023Diffusion Model for Graph Inverse Problems: Towards Effective Source Localization on Complex Networks
56. NeurIPS 2023Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
57. NeurIPS 2023Does a sparse ReLU network training problem always admit an optimum ?
58. NeurIPS 2023Does Graph Distillation See Like Vision Dataset Counterpart?
59. NeurIPS 2023Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
60. NeurIPS 2023Don’t just prune by magnitude! Your mask topology is a secret weapon
61. NeurIPS 2023DSR: Dynamical Surface Representation as Implicit Neural Networks for Protein
62. NeurIPS 2023Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks
63. NeurIPS 2023Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
64. NeurIPS 2023Efficient Learning of Linear Graph Neural Networks via Node Subsampling
65. NeurIPS 2023Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
66. NeurIPS 2023Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity
67. NeurIPS 2023Empowering Convolutional Neural Nets with MetaSin Activation
68. NeurIPS 2023Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
69. NeurIPS 2023Enhancing Sharpness-Aware Optimization Through Variance Suppression
70. NeurIPS 2023Entropic Neural Optimal Transport via Diffusion Processes
71. NeurIPS 2023Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs
72. NeurIPS 2023Equivariant Neural Operator Learning with Graphon Convolution
73. NeurIPS 2023Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
74. NeurIPS 2023Euler-Lagrange Analysis of Generative Adversarial Networks
75. NeurIPS 2023Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
76. NeurIPS 2023Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis
77. NeurIPS 2023Evolving Connectivity for Recurrent Spiking Neural Networks
78. NeurIPS 2023Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
79. NeurIPS 2023Exact Verification of ReLU Neural Control Barrier Functions
80. NeurIPS 2023Explainable Brain Age Prediction using coVariance Neural Networks
81. NeurIPS 2023Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
82. NeurIPS 2023Expressive probabilistic sampling in recurrent neural networks
83. NeurIPS 2023Expressive Sign Equivariant Networks for Spectral Geometric Learning
84. NeurIPS 2023Expressivity-Preserving GNN Simulation
85. NeurIPS 2023Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
86. NeurIPS 2023Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
87. NeurIPS 2023Fair Graph Distillation
88. NeurIPS 2023FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
89. NeurIPS 2023Fine-grained Expressivity of Graph Neural Networks
90. NeurIPS 2023Flat Seeking Bayesian Neural Networks
91. NeurIPS 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
92. NeurIPS 2023From Tempered to Benign Overfitting in ReLU Neural Networks
93. NeurIPS 2023Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
94. NeurIPS 2023Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
95. NeurIPS 2023Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
96. NeurIPS 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
97. NeurIPS 2023Generalised f-Mean Aggregation for Graph Neural Networks
98. NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
99. NeurIPS 2023GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
100. NeurIPS 2023GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels

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.