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

728 papers, page 5 of 15

201. ICML 2023Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
202. ICML 2023Quantitative Universal Approximation Bounds for Deep Belief Networks
203. ICML 2023Randomized Schur Complement Views for Graph Contrastive Learning
204. ICML 2023Relevant Walk Search for Explaining Graph Neural Networks
205. ICML 2023Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
206. ICML 2023Revisiting Structured Variational Autoencoders
207. ICML 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networks
208. ICML 2023RGE: A Repulsive Graph Rectification for Node Classification via Influence
209. ICML 2023RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
210. ICML 2023Scaling Spherical CNNs
211. ICML 2023Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
212. ICML 2023SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching
213. ICML 2023SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
214. ICML 2023SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
215. ICML 2023SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
216. ICML 2023Spatial Implicit Neural Representations for Global-Scale Species Mapping
217. ICML 2023Special Properties of Gradient Descent with Large Learning Rates
218. ICML 2023spred: Solving L1 Penalty with SGD
219. ICML 2023Statistical Foundations of Prior-Data Fitted Networks
220. ICML 2023StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
221. ICML 2023Subequivariant Graph Reinforcement Learning in 3D Environments
222. ICML 2023Taming graph kernels with random features
223. ICML 2023The Acquisition of Physical Knowledge in Generative Neural Networks
224. ICML 2023The Hessian perspective into the Nature of Convolutional Neural Networks
225. ICML 2023Total Variation Graph Neural Networks
226. ICML 2023Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
227. ICML 2023Towards Deep Attention in Graph Neural Networks: Problems and Remedies
228. ICML 2023Towards Robust Graph Incremental Learning on Evolving Graphs
229. ICML 2023Towards Understanding Generalization of Graph Neural Networks
230. ICML 2023Trompt: Towards a Better Deep Neural Network for Tabular Data
231. ICML 2023Under-Counted Tensor Completion with Neural Incorporation of Attributes
232. ICML 2023Understanding Oversquashing in GNNs through the Lens of Effective Resistance
233. ICML 2023Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data
234. ICML 2023Vertical Federated Graph Neural Network for Recommender System
235. ICML 2023Wasserstein Barycenter Matching for Graph Size Generalization of Message Passing Neural Networks
236. ICML 2023What Makes Entities Similar? A Similarity Flooding Perspective for Multi-sourced Knowledge Graph Embeddings
237. ICML 2023When Sparsity Meets Contrastive Models: Less Graph Data Can Bring Better Class-Balanced Representations
238. ICML 2023Width and Depth Limits Commute in Residual Networks
239. ICML 2023XAI Beyond Classification: Interpretable Neural Clustering
240. NeurIPS 2023[Re] $\mathcal{G}$-Mixup: Graph Data Augmentation for Graph Classification
241. NeurIPS 2023[Re] End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
242. NeurIPS 2023[Re] On Explainability of Graph Neural Networks via Subgraph Explorations
243. NeurIPS 2023[Re] Variational Neural Cellular Automata
244. NeurIPS 2023A Bayesian Take on Gaussian Process Networks
245. NeurIPS 2023A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability
246. NeurIPS 2023A Dual-Stream Neural Network Explains the Functional Segregation of Dorsal and Ventral Visual Pathways in Human Brains
247. NeurIPS 2023A fast heuristic to optimize time-space tradeoff for large models
248. NeurIPS 2023A Fractional Graph Laplacian Approach to Oversmoothing
249. NeurIPS 2023A General Framework for Equivariant Neural Networks on Reductive Lie Groups
250. NeurIPS 2023A General Theory of Correct, Incorrect, and Extrinsic Equivariance

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.