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

1,648 papers, page 33 of 33

1601. NeurIPS 2023Three Iterations of (d − 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points
1602. NeurIPS 2023TopoSRL: Topology preserving self-supervised Simplicial Representation Learning
1603. NeurIPS 2023Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs
1604. NeurIPS 2023Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
1605. NeurIPS 2023Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach
1606. NeurIPS 2023Towards Label Position Bias in Graph Neural Networks
1607. NeurIPS 2023Towards Self-Interpretable Graph-Level Anomaly Detection
1608. NeurIPS 2023Train Once and Explain Everywhere: Pre-training Interpretable Graph Neural Networks
1609. NeurIPS 2023Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies
1610. NeurIPS 2023Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
1611. NeurIPS 2023Training Neural Networks is NP-Hard in Fixed Dimension
1612. NeurIPS 2023Training neural operators to preserve invariant measures of chaotic attractors
1613. NeurIPS 2023Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign?
1614. NeurIPS 2023Transfer learning for atomistic simulations using GNNs and kernel mean embeddings
1615. NeurIPS 2023Transformers over Directed Acyclic Graphs
1616. NeurIPS 2023Trial matching: capturing variability with data-constrained spiking neural networks
1617. NeurIPS 2023Truly Scale-Equivariant Deep Nets with Fourier Layers
1618. NeurIPS 2023Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery
1619. NeurIPS 2023Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
1620. NeurIPS 2023Uncovering Neural Scaling Laws in Molecular Representation Learning
1621. NeurIPS 2023Understanding the Limitations of Deep Models for Molecular property prediction: Insights and Solutions
1622. NeurIPS 2023Universal Prompt Tuning for Graph Neural Networks
1623. NeurIPS 2023Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift
1624. NeurIPS 2023Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization
1625. NeurIPS 2023UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures
1626. NeurIPS 2023Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision
1627. NeurIPS 2023Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation
1628. NeurIPS 2023UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction
1629. NeurIPS 2023V-InFoR: A Robust Graph Neural Networks Explainer for Structurally Corrupted Graphs
1630. NeurIPS 2023Variational Annealing on Graphs for Combinatorial Optimization
1631. NeurIPS 2023Variational Monte Carlo on a Budget — Fine-tuning pre-trained Neural Wavefunctions
1632. NeurIPS 2023VeriX: Towards Verified Explainability of Deep Neural Networks
1633. NeurIPS 2023Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements
1634. NeurIPS 2023Volume Feature Rendering for Fast Neural Radiance Field Reconstruction
1635. NeurIPS 2023WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding
1636. NeurIPS 2023Wasserstein distributional robustness of neural networks
1637. NeurIPS 2023Weakly Coupled Deep Q-Networks
1638. NeurIPS 2023What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding
1639. NeurIPS 2023What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models
1640. NeurIPS 2023What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement.
1641. NeurIPS 2023What Planning Problems Can A Relational Neural Network Solve?
1642. NeurIPS 2023When Do Graph Neural Networks Help with Node Classification? Investigating the Homophily Principle on Node Distinguishability
1643. NeurIPS 2023When Do Neural Nets Outperform Boosted Trees on Tabular Data?
1644. NeurIPS 2023White-Box Transformers via Sparse Rate Reduction
1645. NeurIPS 2023Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
1646. NeurIPS 2023Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models
1647. NeurIPS 2023Window-Based Distribution Shift Detection for Deep Neural Networks
1648. NeurIPS 2023Zero-One Laws of Graph 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.