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

148 papers, page 1 of 3

1. ICML 2023A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests
2. ICML 2023A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
3. ICML 2023A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening
4. ICML 2023A Large-Scale Study of Probabilistic Calibration in Neural Network Regression
5. ICML 2023Adaptive Smoothing Gradient Learning for Spiking Neural Networks
6. ICML 2023Additive Causal Bandits with Unknown Graph
7. ICML 2023All in a Row: Compressed Convolution Networks for Graphs
8. ICML 2023Alternately Optimized Graph Neural Networks
9. ICML 2023Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels
10. ICML 2023Bayes-optimal Learning of Deep Random Networks of Extensive-width
11. ICML 2023Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering
12. ICML 2023BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning
13. ICML 2023Boosting Graph Contrastive Learning via Graph Contrastive Saliency
14. ICML 2023Certified Robust Neural Networks: Generalization and Corruption Resistance
15. ICML 2023CircuitNet: A Generic Neural Network to Realize Universal Circuit Motif Modeling
16. ICML 2023CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
17. ICML 2023Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities
18. ICML 2023Conditional Graph Information Bottleneck for Molecular Relational Learning
19. ICML 2023Conformal Inference is (almost) Free for Neural Networks Trained with Early Stopping
20. ICML 2023Conformal Prediction Sets for Graph Neural Networks
21. ICML 2023Controllable Neural Symbolic Regression
22. ICML 2023Deep Graph Representation Learning and Optimization for Influence Maximization
23. ICML 2023Deep linear networks can benignly overfit when shallow ones do
24. ICML 2023Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks
25. ICML 2023Detecting Adversarial Directions in Deep Reinforcement Learning to Make Robust Decisions
26. ICML 2023Deterministic equivalent and error universality of deep random features learning
27. ICML 2023Direct Parameterization of Lipschitz-Bounded Deep Networks
28. ICML 2023Disentangled Multiplex Graph Representation Learning
29. ICML 2023Distribution Free Prediction Sets for Node Classification
30. ICML 2023Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
31. ICML 2023Does a Neural Network Really Encode Symbolic Concepts?
32. ICML 2023Does Continual Learning Equally Forget All Parameters?
33. ICML 2023Dropout Reduces Underfitting
34. ICML 2023Dual Propagation: Accelerating Contrastive Hebbian Learning with Dyadic Neurons
35. ICML 2023ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
36. ICML 2023Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical Theories
37. ICML 2023Efficient Graph Field Integrators Meet Point Clouds
38. ICML 2023Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network
39. ICML 2023Efficient Parametric Approximations of Neural Network Function Space Distance
40. ICML 2023Efficiently predicting high resolution mass spectra with graph neural networks
41. ICML 2023Equivariance with Learned Canonicalization Functions
42. ICML 2023Equivariant Polynomials for Graph Neural Networks
43. ICML 2023Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks
44. ICML 2023Expectation-Complete Graph Representations with Homomorphisms
45. ICML 2023Feature Expansion for Graph Neural Networks
46. ICML 2023Featured Graph Coarsening with Similarity Guarantees
47. ICML 2023Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
48. ICML 2023From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
49. ICML 2023Function-Space Regularization in Neural Networks: A Probabilistic Perspective
50. ICML 2023GC-Flow: A Graph-Based Flow Network for Effective Clustering

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.