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 28 of 33

1351. NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
1352. NeurIPS 2023Generating Behaviorally Diverse Policies with Latent Diffusion Models
1353. NeurIPS 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEs
1354. NeurIPS 2023GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
1355. NeurIPS 2023GEQ: Gaussian Kernel Inspired Equilibrium Models
1356. NeurIPS 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
1357. NeurIPS 2023GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
1358. NeurIPS 2023Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization
1359. NeurIPS 2023Globally injective and bijective neural operators
1360. NeurIPS 2023GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
1361. NeurIPS 2023Goal-conditioned Offline Planning from Curious Exploration
1362. NeurIPS 2023Going beyond persistent homology using persistent homology
1363. NeurIPS 2023GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
1364. NeurIPS 2023Gradient-Based Feature Learning under Structured Data
1365. NeurIPS 2023Graph Clustering with Graph Neural Networks
1366. NeurIPS 2023Graph Contrastive Learning with Stable and Scalable Spectral Encoding
1367. NeurIPS 2023Graph Convolutional Kernel Machine versus Graph Convolutional Networks
1368. NeurIPS 2023Graph Denoising Diffusion for Inverse Protein Folding
1369. NeurIPS 2023Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
1370. NeurIPS 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis
1371. NeurIPS 2023Graph of Circuits with GNN for Exploring the Optimal Design Space
1372. NeurIPS 2023Graph-Structured Gaussian Processes for Transferable Graph Learning
1373. NeurIPS 2023GraphMP: Graph Neural Network-based Motion Planning with Efficient Graph Search
1374. NeurIPS 2023GSLB: The Graph Structure Learning Benchmark
1375. NeurIPS 2023GUST: Combinatorial Generalization by Unsupervised Grouping with Neuronal Coherence
1376. NeurIPS 2023Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition
1377. NeurIPS 2023Hierarchical Randomized Smoothing
1378. NeurIPS 2023High dimensional, tabular deep learning with an auxiliary knowledge graph
1379. NeurIPS 2023HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
1380. NeurIPS 2023How Does Adaptive Optimization Impact Local Neural Network Geometry?
1381. NeurIPS 2023HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork
1382. NeurIPS 2023Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach
1383. NeurIPS 2023Hypernetwork-based Meta-Learning for Low-Rank Physics-Informed Neural Networks
1384. NeurIPS 2023Hypervolume Maximization: A Geometric View of Pareto Set Learning
1385. NeurIPS 2023Implicit Bias of (Stochastic) Gradient Descent for Rank-1 Linear Neural Network
1386. NeurIPS 2023Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data
1387. NeurIPS 2023Implicit Manifold Gaussian Process Regression
1388. NeurIPS 2023Implicit Transfer Operator Learning: Multiple Time-Resolution Models for Molecular Dynamics
1389. NeurIPS 2023Improvements on Uncertainty Quantification for Node Classification via Distance Based Regularization
1390. NeurIPS 2023Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings
1391. NeurIPS 2023Improving Graph Matching with Positional Reconstruction Encoder-Decoder Network
1392. NeurIPS 2023Improving neural network representations using human similarity judgments
1393. NeurIPS 2023Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training
1394. NeurIPS 2023Individualized Dosing Dynamics via Neural Eigen Decomposition
1395. NeurIPS 2023Information Geometry of the Retinal Representation Manifold
1396. NeurIPS 2023Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks
1397. NeurIPS 2023Inner Product-based Neural Network Similarity
1398. NeurIPS 2023Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks
1399. NeurIPS 2023Interpretable Graph Networks Formulate Universal Algebra Conjectures
1400. NeurIPS 2023Interpretable Prototype-based Graph Information Bottleneck

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.