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

489 papers, page 6 of 10

251. NeurIPS 2023Large sample spectral analysis of graph-based multi-manifold clustering
252. NeurIPS 2023LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer
253. NeurIPS 2023Latent Field Discovery in Interacting Dynamical Systems with Neural Fields
254. NeurIPS 2023Latent Graph Inference with Limited Supervision
255. NeurIPS 2023Layer-Neighbor Sampling --- Defusing Neighborhood Explosion in GNNs
256. NeurIPS 2023LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings
257. NeurIPS 2023Learning and processing the ordinal information of temporal sequences in recurrent neural circuits
258. NeurIPS 2023Learning DAGs from Data with Few Root Causes
259. NeurIPS 2023Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization
260. NeurIPS 2023Learning Efficient Surrogate Dynamic Models with Graph Spline Networks
261. NeurIPS 2023Learning Functional Transduction
262. NeurIPS 2023Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective
263. NeurIPS 2023Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
264. NeurIPS 2023Learning Large Graph Property Prediction via Graph Segment Training
265. NeurIPS 2023Learning Large-scale Neural Fields via Context Pruned Meta-Learning
266. NeurIPS 2023Learning Layer-wise Equivariances Automatically using Gradients
267. NeurIPS 2023Learning Provably Robust Estimators for Inverse Problems via Jittering
268. NeurIPS 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecification
269. NeurIPS 2023Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
270. NeurIPS 2023Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
271. NeurIPS 2023Learning threshold neurons via edge of stability
272. NeurIPS 2023Learning To Dive In Branch And Bound
273. NeurIPS 2023Learning Trajectories are Generalization Indicators
274. NeurIPS 2023Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
275. NeurIPS 2023Leveraging the two-timescale regime to demonstrate convergence of neural networks
276. NeurIPS 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
277. NeurIPS 2023LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing
278. NeurIPS 2023Locality-Aware Generalizable Implicit Neural Representation
279. NeurIPS 2023LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
280. NeurIPS 2023Long Sequence Hopfield Memory
281. NeurIPS 2023Lovász Principle for Unsupervised Graph Representation Learning
282. NeurIPS 2023Low Tensor Rank Learning of Neural Dynamics
283. NeurIPS 2023MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
284. NeurIPS 2023Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
285. NeurIPS 2023Max-Sliced Mutual Information
286. NeurIPS 2023Maximum Independent Set: Self-Training through Dynamic Programming
287. NeurIPS 2023MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy
288. NeurIPS 2023Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference
289. NeurIPS 2023Meta-Learning with Neural Bandit Scheduler
290. NeurIPS 2023MIMEx: Intrinsic Rewards from Masked Input Modeling
291. NeurIPS 2023Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective
292. NeurIPS 2023MLFMF: Data Sets for Machine Learning for Mathematical Formalization
293. NeurIPS 2023MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability
294. NeurIPS 2023Modulated Neural ODEs
295. NeurIPS 2023Module-wise Training of Neural Networks via the Minimizing Movement Scheme
296. NeurIPS 2023Multi-task Graph Neural Architecture Search with Task-aware Collaboration and Curriculum
297. NeurIPS 2023MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks
298. NeurIPS 2023Multiplication-Free Transformer Training via Piecewise Affine Operations
299. NeurIPS 2023MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
300. NeurIPS 2023NAR-Former V2: Rethinking Transformer for Universal Neural Network Representation Learning

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.