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 17 of 33

801. NeurIPS 2024EGonc : Energy-based Open-Set Node Classification with substitute Unknowns
802. NeurIPS 2024EGSST: Event-based Graph Spatiotemporal Sensitive Transformer for Object Detection
803. NeurIPS 2024Energy-based Epistemic Uncertainty for Graph Neural Networks
804. NeurIPS 2024Enhancing Chess Reinforcement Learning with Graph Representation
805. NeurIPS 2024Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
806. NeurIPS 2024Enhancing Robustness of Graph Neural Networks on Social Media with Explainable Inverse Reinforcement Learning
807. NeurIPS 2024Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters
808. NeurIPS 2024Even Sparser Graph Transformers
809. NeurIPS 2024Expected Probabilistic Hierarchies
810. NeurIPS 2024Exploitation of a Latent Mechanism in Graph Contrastive Learning: Representation Scattering
811. NeurIPS 2024Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks
812. NeurIPS 2024Extracting Training Data from Molecular Pre-trained Models
813. NeurIPS 2024Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior
814. NeurIPS 2024Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification
815. NeurIPS 2024Faster Local Solvers for Graph Diffusion Equations
816. NeurIPS 2024Federated Graph Learning for Cross-Domain Recommendation
817. NeurIPS 2024FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
818. NeurIPS 2024FUG: Feature-Universal Graph Contrastive Pre-training for Graphs with Diverse Node Features
819. NeurIPS 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
820. NeurIPS 2024GDeR: Safeguarding Efficiency, Balancing, and Robustness via Prototypical Graph Pruning
821. NeurIPS 2024Generalizing CNNs to graphs with learnable neighborhood quantization
822. NeurIPS 2024Generative Semi-supervised Graph Anomaly Detection
823. NeurIPS 2024Geometric Trajectory Diffusion Models
824. NeurIPS 2024Geometry Awakening: Cross-Geometry Learning Exhibits Superiority over Individual Structures
825. NeurIPS 2024GFT: Graph Foundation Model with Transferable Tree Vocabulary
826. NeurIPS 2024GLBench: A Comprehensive Benchmark for Graph with Large Language Models
827. NeurIPS 2024Gradient Rewiring for Editable Graph Neural Network Training
828. NeurIPS 2024GRANOLA: Adaptive Normalization for Graph Neural Networks
829. NeurIPS 2024Graph Classification via Reference Distribution Learning: Theory and Practice
830. NeurIPS 2024Graph Coarsening with Message-Passing Guarantees
831. NeurIPS 2024Graph Convolutions Enrich the Self-Attention in Transformers!
832. NeurIPS 2024Graph Edit Distance with General Costs Using Neural Set Divergence
833. NeurIPS 2024Graph Learning for Numeric Planning
834. NeurIPS 2024Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Series
835. NeurIPS 2024Graph Neural Networks and Arithmetic Circuits
836. NeurIPS 2024Graph neural networks and non-commuting operators
837. NeurIPS 2024Graph Neural Networks Do Not Always Oversmooth
838. NeurIPS 2024Graph Neural Networks Need Cluster-Normalize-Activate Modules
839. NeurIPS 2024Graph Structure Inference with BAM: Neural Dependency Processing via Bilinear Attention
840. NeurIPS 2024Graphcode: Learning from multiparameter persistent homology using graph neural networks
841. NeurIPS 2024GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction
842. NeurIPS 2024GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
843. NeurIPS 2024GraphTrail: Translating GNN Predictions into Human-Interpretable Logical Rules
844. NeurIPS 2024Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba
845. NeurIPS 2024HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation
846. NeurIPS 2024HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
847. NeurIPS 2024HGDL: Heterogeneous Graph Label Distribution Learning
848. NeurIPS 2024If You Want to Be Robust, Be Wary of Initialization
849. NeurIPS 2024Improving Generalization of Dynamic Graph Learning via Environment Prompt
850. NeurIPS 2024Improving Temporal Link Prediction via Temporal Walk Matrix Projection

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.