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

76 papers, page 2 of 2

51. ICML 2024Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
52. ICML 2024On the Expressive Power of Spectral Invariant Graph Neural Networks
53. ICML 2024On the Generalization of Equivariant Graph Neural Networks
54. ICML 2024On the Role of Edge Dependency in Graph Generative Models
55. ICML 2024PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
56. ICML 2024Perfect Alignment May be Poisonous to Graph Contrastive Learning
57. ICML 2024Position: Future Directions in the Theory of Graph Machine Learning
58. ICML 2024Position: Graph Foundation Models Are Already Here
59. ICML 2024Position: Relational Deep Learning - Graph Representation Learning on Relational Databases
60. ICML 2024Probabilistic Routing for Graph-Based Approximate Nearest Neighbor Search
61. ICML 2024Quantum Positional Encodings for Graph Neural Networks
62. ICML 2024Recurrent Distance Filtering for Graph Representation Learning
63. ICML 2024Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
64. ICML 2024Robust Graph Matching when Nodes are Corrupt
65. ICML 2024S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning
66. ICML 2024Sign Rank Limitations for Inner Product Graph Decoders
67. ICML 2024Simulation of Graph Algorithms with Looped Transformers
68. ICML 2024SLOG: An Inductive Spectral Graph Neural Network Beyond Polynomial Filter
69. ICML 2024Structure Your Data: Towards Semantic Graph Counterfactuals
70. ICML 2024Surprisingly Strong Performance Prediction with Neural Graph Features
71. ICML 2024The Expressive Power of Path-Based Graph Neural Networks
72. ICML 2024Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
73. ICML 2024Understanding Heterophily for Graph Neural Networks
74. ICML 2024Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
75. ICML 2024Unsupervised Episode Generation for Graph Meta-learning
76. ICML 2024VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context

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.