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

842 papers, page 16 of 17

751. NeurIPS 2023Scale-Space Hypernetworks for Efficient Biomedical Image Analysis
752. NeurIPS 2023Scale-teaching: Robust Multi-scale Training for Time Series Classification with Noisy Labels
753. NeurIPS 2023Scaling Laws for Hyperparameter Optimization
754. NeurIPS 2023Self-supervised Graph Neural Networks via Low-Rank Decomposition
755. NeurIPS 2023Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
756. NeurIPS 2023Separable Physics-Informed Neural Networks
757. NeurIPS 2023SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations
758. NeurIPS 2023Sharpness-Aware Minimization Leads to Low-Rank Features
759. NeurIPS 2023Sheaf Hypergraph Networks
760. NeurIPS 2023Should Under-parameterized Student Networks Copy or Average Teacher Weights?
761. NeurIPS 2023Simple and Asymmetric Graph Contrastive Learning without Augmentations
762. NeurIPS 2023Simplicity Bias in 1-Hidden Layer Neural Networks
763. NeurIPS 2023Simplifying Neural Network Training Under Class Imbalance
764. NeurIPS 2023Simultaneous embedding of multiple attractor manifolds in a recurrent neural network using constrained gradient optimization
765. NeurIPS 2023Sparse Graph Learning from Spatiotemporal Time Series
766. NeurIPS 2023Spectral Evolution and Invariance in Linear-width Neural Networks
767. NeurIPS 2023Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
768. NeurIPS 2023Spike-driven Transformer
769. NeurIPS 2023Spiking PointNet: Spiking Neural Networks for Point Clouds
770. NeurIPS 2023Squared Neural Families: A New Class of Tractable Density Models
771. NeurIPS 2023Statistical Analysis of Quantum State Learning Process in Quantum Neural Networks
772. NeurIPS 2023Statistically Valid Variable Importance Assessment through Conditional Permutations
773. NeurIPS 2023Stochastic Approximation Algorithms for Systems of Interacting Particles
774. NeurIPS 2023Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
775. NeurIPS 2023Structure Learning with Adaptive Random Neighborhood Informed MCMC
776. NeurIPS 2023Structure of universal formulas
777. NeurIPS 2023Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
778. NeurIPS 2023Structured Neural Networks for Density Estimation and Causal Inference
779. NeurIPS 2023SyncTREE: Fast Timing Analysis for Integrated Circuit Design through a Physics-informed Tree-based Graph Neural Network
780. NeurIPS 2023Tailoring Self-Attention for Graph via Rooted Subtrees
781. NeurIPS 2023Task-aware Distributed Source Coding under Dynamic Bandwidth
782. NeurIPS 2023Template-free Articulated Neural Point Clouds for Reposable View Synthesis
783. NeurIPS 2023TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
784. NeurIPS 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphs
785. NeurIPS 2023TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
786. NeurIPS 2023The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
787. NeurIPS 2023The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
788. NeurIPS 2023The expressive power of pooling in Graph Neural Networks
789. NeurIPS 2023The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models
790. NeurIPS 2023The Quantization Model of Neural Scaling
791. NeurIPS 2023The Separation Capacity of Random Neural Networks
792. NeurIPS 2023The Transient Nature of Emergent In-Context Learning in Transformers
793. NeurIPS 2023Theoretical Analysis of the Inductive Biases in Deep Convolutional Networks
794. NeurIPS 2023This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
795. NeurIPS 2023Three Iterations of (d − 1)-WL Test Distinguish Non Isometric Clouds of d-dimensional Points
796. NeurIPS 2023TopoSRL: Topology preserving self-supervised Simplicial Representation Learning
797. NeurIPS 2023Towards a Comprehensive Benchmark for High-Level Synthesis Targeted to FPGAs
798. NeurIPS 2023Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
799. NeurIPS 2023Towards Combinatorial Generalization for Catalysts: A Kohn-Sham Charge-Density Approach
800. NeurIPS 2023Towards Label Position Bias in 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.