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

533 papers, page 1 of 11

1. ICML 2026A Graph Foundation Model with Cross-Modal Alignment and Modality-Aware Expert Fusion for Multi-Modal Graphs
2. ICML 2026A Graphop Analysis of Graph Neural Networks on Sparse Graphs: Generalization and Universal Approximation
3. ICML 2026A Unifying Relational Perspective on Expressive Lottery Tickets
4. ICML 2026Adaptive Memory Retention in Dynamic Graphs
5. ICML 2026Adaptive Node Feature Selection for Graph Neural Networks
6. ICML 2026Adaptive Recurrent Message Passing for Test Time Computing on Graphs
7. ICML 2026Adversarial Attacks and Robust Training for Hypergraph Neural Networks
8. ICML 2026Aitchison Embeddings for Learning Compositional Graph Representations
9. ICML 2026Amortized Maximum Inner Product Search with Learned Support Functions
10. ICML 2026An Approximation Algorithm for Graph Label Selection
11. ICML 2026Anchor-guided Hypergraph Condensation with Dual-level Discrimination
12. ICML 2026Any-dimensional invariant universality
13. ICML 2026Approximate Nearest Neighbor Search for Modern AI: A Projection-Augmented Graph Approach
14. ICML 2026Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles
15. ICML 2026Backward Oversmoothing: why is it hard to train deep Graph Neural Networks?
16. ICML 2026Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
17. ICML 2026Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors
18. ICML 2026Beyond Tokens: Enhancing RTL Quality Estimation via Structural Graph Learning
19. ICML 2026Bias-Spectrum Neural Processes for Parametric PDEs: Architecture Priors Meet PDE Constraints
20. ICML 2026Bipartite Graph Attention-based Clustering for Large-scale scRNA-seq Data
21. ICML 2026BOCLOAK: Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection
22. ICML 2026Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement
23. ICML 2026Brain Networks Should Be Learned, Not Constructed
24. ICML 2026Bridging Structure and Semantics: Uncertainty-Modulated Dual-Path Diffusion for Robust Text-Attributed Graph Learning
25. ICML 2026CAffNet: Hard Constraint-Affine Neural Networks
26. ICML 2026CalPro: Prior-Aware Evidential Conformal Prediction with Structure-Aware Sensitivity Bounds for Protein Structures
27. ICML 2026Can Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
28. ICML 2026Capacity-Agnostic Parameter Isolation for Continual Graph Learning
29. ICML 2026CCLRec: Consensus-driven Contrastive Learning for LLM-enhanced Graph Recommendation
30. ICML 2026CELL: A Causal Perspective for Fairness-aware Graph Adaptation
31. ICML 2026CLINIC: Towards High-quality Graph Out-Of-Distribution Detection
32. ICML 2026Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning
33. ICML 2026CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning
34. ICML 2026D$^3$: Dynamic Directional Graph-Constrained Data Scheduling for LLM Training
35. ICML 2026Delving into Non-Exchangeability for Conformal Prediction in Graph-Structured Multivariate Time Series
36. ICML 2026DiP-G: Discrete Prompting for Graph Neural Networks
37. ICML 2026Directional Neural Collapse Explains Few-Shot Transfer in Self-Supervised Learning
38. ICML 2026Discriminative Attribute Graph Clustering Through Topology-Guided Contrastive Learning
39. ICML 2026Discriminative Mixture-of-Experts on Graphs with Reliable Expert Fusion
40. ICML 2026DREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise
41. ICML 2026Dual-channel Dynamic Graph Neural Networks with Adaptive Adjacency Learning and Multi-scale Representation Fusion
42. ICML 2026E2Former-V2: On-the-Fly Equivariant Attention with Linear Activation Memory
43. ICML 2026Edge-colored Clustering in Hypergraphs: A MaxECC Approximation
44. ICML 2026Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch
45. ICML 2026Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
46. ICML 2026Entangled No More: Multi-Domain Decoupling for Robust Dynamic Graph Neural Networks
47. ICML 2026EpiTwin: Spatiotemporal Graph Transformers for Epileptic sEEG Signal Reconstruction
48. ICML 2026EqGINO: Equivariant Geometry-Informed Fourier Neural Operators for 3D PDEs
49. ICML 2026EquiCAD: A Geometric Equivariant Neural Network for 3D Shape Classification
50. ICML 2026ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed 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.