Deep learning · graph neural networks papers, 2023–2026

Key findings

• Deep learning · graph neural networks fell from 728 papers in 2023 to 326 in 2026, so interest is cooling.
• NeurIPS publishes the most Deep learning · graph neural networks work: 842 papers from 2023 to 2026, 51% of the four-venue total.
• The fastest-growing overlap is with Datasets and Benchmarks: papers tagged with both rose from 9 in 2023 to 20 in 2026.
• Among the 11 topics under Deep Learning, it ranks #5 by 2026 paper count; the fastest grower in the group, Diffusion Models, is up 4.1x versus 0.4x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A New Perspective on Target-Conditioned Structural Dynamics for Link Prediction in Dynamic Graphs
• ICML 2026A Graph Foundation Model with Cross-Modal Alignment and Modality-Aware Expert Fusion for Multi-Modal Graphs
• ICLR 2026Laplacian Kernelized Bandit
• CVPR 2026Gamba Mamba-based graph convolutional network with dynamic graph topology learning
• NeurIPS 2025Depth-Width Tradeoffs for Transformers on Graph Tasks
• ICML 2025A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
• ICLR 2025Factor Graph-based Interpretable Neural Networks
• CVPR 2025Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
• NeurIPS 2024[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks
• ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
• ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
• CVPR 2024Learning Structure-from-Motion with Graph Attention Networks
• NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
• ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
• ICLR 2023Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
• CVPR 2023An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity
• NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
• ICML 2026A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
• ICLR 2026GTool: Graph Enhanced Tool Planning with Large Language Model
• CVPR 2026GSNR Graph Smooth Null-Space Representation for Inverse Problems

All Deep learning · graph neural networks papers

160 papers, page 2 of 4

51. NeurIPS 2024Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks
52. NeurIPS 2024Extracting Training Data from Molecular Pre-trained Models
53. NeurIPS 2024Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
54. NeurIPS 2024Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification
55. NeurIPS 2024Faster Local Solvers for Graph Diffusion Equations
56. NeurIPS 2024Federated Graph Learning for Cross-Domain Recommendation
57. NeurIPS 2024FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
58. NeurIPS 2024FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features
59. NeurIPS 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
60. NeurIPS 2024GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
61. NeurIPS 2024Generalizing CNNs to graphs with learnable neighborhood quantization
62. NeurIPS 2024Generative Semi-supervised Graph Anomaly Detection
63. NeurIPS 2024Geometric Trajectory Diffusion Models
64. NeurIPS 2024Geometry Awakening: Cross-Geometry Learning Exhibits Superiority over Individual Structures
65. NeurIPS 2024GFT: Graph Foundation Model with Transferable Tree Vocabulary
66. NeurIPS 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Models
67. NeurIPS 2024Gradient Rewiring for Editable Graph Neural Network Training
68. NeurIPS 2024GRANOLA: Adaptive Normalization for Graph Neural Networks
69. NeurIPS 2024Graph Classification via Reference Distribution Learning: Theory and Practice
70. NeurIPS 2024Graph Coarsening with Message-Passing Guarantees
71. NeurIPS 2024Graph Convolutions Enrich the Self-Attention in Transformers!
72. NeurIPS 2024Graph Edit Distance with General Costs Using Neural Set Divergence
73. NeurIPS 2024Graph Learning for Numeric Planning
74. NeurIPS 2024Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series
75. NeurIPS 2024Graph Neural Networks and Arithmetic Circuits
76. NeurIPS 2024Graph neural networks and non-commuting operators
77. NeurIPS 2024Graph Neural Networks Do Not Always Oversmooth
78. NeurIPS 2024Graph Neural Networks Need Cluster-Normalize-Activate Modules
79. NeurIPS 2024Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
80. NeurIPS 2024Graphcode: Learning from multiparameter persistent homology using graph neural networks
81. NeurIPS 2024GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction
82. NeurIPS 2024GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
83. NeurIPS 2024GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules
84. NeurIPS 2024Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba
85. NeurIPS 2024HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation
86. NeurIPS 2024HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
87. NeurIPS 2024HGDL: Heterogeneous Graph Label Distribution Learning
88. NeurIPS 2024If You Want to Be Robust, Be Wary of Initialization
89. NeurIPS 2024Improving Generalization of Dynamic Graph Learning via Environment Prompt
90. NeurIPS 2024Improving Temporal Link Prediction via Temporal Walk Matrix Projection
91. NeurIPS 2024InstructG2I: Synthesizing Images from Multimodal Attributed Graphs
92. NeurIPS 2024Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors
93. NeurIPS 2024IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors
94. NeurIPS 2024Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level
95. NeurIPS 2024Iterative Methods via Locally Evolving Set Process
96. NeurIPS 2024Iteratively Refined Early Interaction Alignment for Subgraph Matching based Graph Retrieval
97. NeurIPS 2024Knowledge Graph Completion by Intermediate Variables Regularization
98. NeurIPS 2024Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases
99. NeurIPS 2024Learning on Large Graphs using Intersecting Communities
100. NeurIPS 2024Learning rigid-body simulators over implicit shapes for large-scale scenes and vision

FAQ

How many Deep learning · graph neural networks papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

1,648 in total: 728 in 2023, 297 in 2024, 297 in 2025, 326 in 2026.

How many Deep learning · graph neural networks papers are at NeurIPS 2026?

NeurIPS 2026 has 102 Deep learning · graph neural networks papers, versus 91 the year before.

How many Deep learning · graph neural networks papers are at ICLR 2026?

ICLR 2026 has 32 Deep learning · graph neural networks papers, versus 50 the year before.

How many Deep learning · graph neural networks papers are at ICML 2026?

ICML 2026 has 168 Deep learning · graph neural networks papers, versus 141 the year before.

How many Deep learning · graph neural networks papers are at CVPR 2026?

CVPR 2026 has 24 Deep learning · graph neural networks papers, versus 15 the year before.

Which conference publishes the most Deep learning · graph neural networks papers?

NeurIPS, with 842 papers from 2023 to 2026.