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

141 papers, page 2 of 3

51. ICML 2025G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
52. ICML 2025GCAL: Adapting Graph Models to Evolving Domain Shifts
53. ICML 2025Geometric Hyena Networks for Large-scale Equivariant Learning
54. ICML 2025GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning
55. ICML 2025Graph Adaptive Autoregressive Moving Average Models
56. ICML 2025Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models
57. ICML 2025Graph Generative Pre-trained Transformer
58. ICML 2025Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation
59. ICML 2025Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
60. ICML 2025Graph-Based Algorithms for Diverse Similarity Search
61. ICML 2025Graph4MM: Weaving Multimodal Learning with Structural Information
62. ICML 2025GraphGPT: Generative Pre-trained Graph Eulerian Transformer
63. ICML 2025Griffin: Towards a Graph-Centric Relational Database Foundation Model
64. ICML 2025GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
65. ICML 2025Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
66. ICML 2025Hgformer: Hyperbolic Graph Transformer for Collaborative Filtering
67. ICML 2025HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport
68. ICML 2025Hierarchical Graph Tokenization for Molecule-Language Alignment
69. ICML 2025How Expressive are Knowledge Graph Foundation Models?
70. ICML 2025How Much Can Transfer? BRIDGE: Bounded Multi-Domain Graph Foundation Model with Generalization Guarantees
71. ICML 2025Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations
72. ICML 2025HyperNear: Unnoticeable Node Injection Attacks on Hypergraph Neural Networks
73. ICML 2025Implicit degree bias in the link prediction task
74. ICML 2025Implicit Subgraph Neural Network
75. ICML 2025Improved Approximations for Hard Graph Problems using Predictions
76. ICML 2025Improved Expressivity of Hypergraph Neural Networks through High-Dimensional Generalized Weisfeiler-Leman Algorithms
77. ICML 2025Improving Soft Unification with Knowledge Graph Embedding Methods
78. ICML 2025Improving the Effective Receptive Field of Message-Passing Neural Networks
79. ICML 2025iN2V: Bringing Transductive Node Embeddings to Inductive Graphs
80. ICML 2025Instance Correlation Graph-based Naive Bayes
81. ICML 2025Large Language-Geometry Model: When LLM meets Equivariance
82. ICML 2025Learn Beneficial Noise as Graph Augmentation
83. ICML 2025Learnable Spatial-Temporal Positional Encoding for Link Prediction
84. ICML 2025Learning Latent Graph Structures and their Uncertainty
85. ICML 2025Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective
86. ICML 2025LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
87. ICML 2025Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
88. ICML 2025ML$^2$-GCL: Manifold Learning Inspired Lightweight Graph Contrastive Learning
89. ICML 2025Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment
90. ICML 2025Multi-View Graph Clustering via Node-Guided Contrastive Encoding
91. ICML 2025N2GON: Neural Networks for Graph-of-Net with Position Awareness
92. ICML 2025No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets
93. ICML 2025Nonparametric Teaching for Graph Property Learners
94. ICML 2025On Measuring Long-Range Interactions in Graph Neural Networks
95. ICML 2025Open Your Eyes: Vision Enhances Message Passing Neural Networks in Link Prediction
96. ICML 2025Outlier-Aware Post-Training Quantization for Discrete Graph Diffusion Models
97. ICML 2025PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities
98. ICML 2025Position: Graph Matching Systems Deserve Better Benchmarks
99. ICML 2025Position: Spectral GNNs Rely Less on Graph Fourier Basis than Conceived
100. ICML 2025Positional Encoding meets Persistent Homology on Graphs

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.