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

489 papers, page 1 of 10

1. NeurIPS 2023[Re] $\mathcal{G}$-Mixup: Graph Data Augmentation for Graph Classification
2. NeurIPS 2023[Re] End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
3. NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
4. NeurIPS 2023[Re] Variational Neural Cellular Automata
5. NeurIPS 2023A Bayesian Take on Gaussian Process Networks
6. NeurIPS 2023A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
7. NeurIPS 2023A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains
8. NeurIPS 2023A fast heuristic to optimize time-space tradeoff for large models
9. NeurIPS 2023A Fractional Graph Laplacian Approach to Oversmoothing
10. NeurIPS 2023A General Framework for Equivariant Neural Networks on Reductive Lie Groups
11. NeurIPS 2023A General Theory of Correct, Incorrect, and Extrinsic Equivariance
12. NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
13. NeurIPS 2023A graphon-signal analysis of graph neural networks
14. NeurIPS 2023A Metadata-Driven Approach to Understand Graph Neural Networks
15. NeurIPS 2023A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
16. NeurIPS 2023A new perspective on building efficient and expressive 3D equivariant graph neural networks
17. NeurIPS 2023A polar prediction model for learning to represent visual transformations
18. NeurIPS 2023A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation
19. NeurIPS 2023A Scalable Neural Network for DSIC Affine Maximizer Auction Design
20. NeurIPS 2023A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time
21. NeurIPS 2023A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
22. NeurIPS 2023Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism
23. NeurIPS 2023Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation
24. NeurIPS 2023Accelerating Molecular Graph Neural Networks via Knowledge Distillation
25. NeurIPS 2023Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement
26. NeurIPS 2023Active Learning-Based Species Range Estimation
27. NeurIPS 2023Active Negative Loss Functions for Learning with Noisy Labels
28. NeurIPS 2023Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
29. NeurIPS 2023Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds
30. NeurIPS 2023Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
31. NeurIPS 2023Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
32. NeurIPS 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
33. NeurIPS 2023Affinity-Aware Graph Networks
34. NeurIPS 2023AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
35. NeurIPS 2023Aiming towards the minimizers: fast convergence of SGD for overparametrized problems
36. NeurIPS 2023AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity
37. NeurIPS 2023An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations
38. NeurIPS 2023An Inductive Bias for Tabular Deep Learning
39. NeurIPS 2023An Information Theory Perspective on Variance-Invariance-Covariance Regularization
40. NeurIPS 2023Analysis of Variance of Multiple Causal Networks
41. NeurIPS 2023Analyzing Generalization of Neural Networks through Loss Path Kernels
42. NeurIPS 2023Anchor Data Augmentation
43. NeurIPS 2023Approximately Equivariant Graph Networks
44. NeurIPS 2023Approximation-Generalization Trade-offs under (Approximate) Group Equivariance
45. NeurIPS 2023Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
46. NeurIPS 2023Are aligned neural networks adversarially aligned?
47. NeurIPS 2023Are GATs Out of Balance?
48. NeurIPS 2023ARTree: A Deep Autoregressive Model for Phylogenetic Inference
49. NeurIPS 2023ASPEN: Breaking Operator Barriers for Efficient Parallelization of Deep Neural Networks
50. NeurIPS 2023Asymmetric Certified Robustness via Feature-Convex Neural Networks

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.