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

326 papers, page 7 of 7

301. NeurIPS 2026Oversmoothing as Representation Degeneracy in Neural Sheaf Diffusion
302. NeurIPS 2026PANDA: Prior-guided Attentional Dual-path Architecture
303. NeurIPS 2026PATH: A Dual Perspective for High-quality Text-attributed Graph Learning
304. NeurIPS 2026Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
305. NeurIPS 2026Pointwise Lipschitz Continuous Graph Algorithms
306. NeurIPS 2026Practical Non-Stationary Graph Gaussian Processes
307. NeurIPS 2026R-GRec: Relation-Guided Generative Recommendation via Collaborative Graph Supervision
308. NeurIPS 2026Relation-Aware Graph Foundation Model
309. NeurIPS 2026Releasing Anchors from Cross-View Correspondence: Probabilistic Multi-View Anchor Graph Clustering
310. NeurIPS 2026Return to Basics: Very Simple Graph Contrastive Learning via Noise Cancellation Principle
311. NeurIPS 2026Robust Graph Diffusion Model
312. NeurIPS 2026Robust Surrogate Modeling for Explainable Graph Neural Networks
313. NeurIPS 2026S&P: Towards Scalable and Powerful Graph Learning with Hierarchical Structural Acquisition
314. NeurIPS 2026Self-Tuning Graph Filters via State-Dependent Operator Composition
315. NeurIPS 2026Semantically Complementary Spectral Views Learning for Graph-Level Anomaly Detection
316. NeurIPS 2026SGNNBench: A Holistic Evaluation of Spiking Graph Neural Networks on Large-scale Graphs
317. NeurIPS 2026Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks
318. NeurIPS 2026Spectral Reversal: Counteracting Singular Value Bias for Graph Prompting
319. NeurIPS 2026SSDGExplainer: Structure-Semantic Dual-Guided Explainer for Graph Neural Networks
320. NeurIPS 2026Towards Universal Black-box Attacks on Graph Neural Networks
321. NeurIPS 2026UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning
322. NeurIPS 2026Uncertainty-Aware Fuzzy Graph Contrastive Learning
323. NeurIPS 2026WavCIL: Wavelet Coefficient-Domain Invariant Learning for Dynamic Graph OOD Generalization
324. NeurIPS 2026Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them
325. NeurIPS 2026When Graph Structure Provably Helps Classification: Non-Asymptotic Recovery Guarantees
326. NeurIPS 2026xVGAE: A Hierarchical Variational Graph Autoencoder for Exchangeable 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.