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 5 of 33

201. ICML 2026Sparse Bayesian Deep Functional Learning with Structured Region Selection
202. ICML 2026Spike-HTR: Spiking Neural Transformer for Handwritten Text Recognition
203. ICML 2026ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
204. ICML 2026Structure-Centric Graph Foundation Model via Geometric Bases
205. ICML 2026Subspace-Aware Feature Reshaping for Open-Set Graph Class-Incremental Learning
206. ICML 2026SuperHype: Hypergraph Generation via Graph-Superposition Decomposition
207. ICML 2026Supervised Graph Contrastive Learning for Gene Regulatory Networks
208. ICML 2026SWING: Unlocking Implicit Graph Representations for Graph Random Features
209. ICML 2026Target-Oriented Pretraining Data Selection via Neuron-Activated Graph
210. ICML 2026TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
211. ICML 2026Towards Generative Graph Matching for Graph Edit Distance Computation
212. ICML 2026Towards Hierarchy–Uniformity Equilibrium: Recovering Semantic Depth in Hypergraph Contrastive Learning
213. ICML 2026Transformers Provably Learn Algorithmic Solutions for Graph Connectivity, But Only with the Right Data
214. ICML 2026Uncertainty-Constrained Trustworthiness for Graph Learning
215. ICML 2026Understanding Truncated Positional Encodings for Graph Neural Networks
216. ICML 2026Unitary Convolutions for Message-passing and Positional Encodings on Directed Graphs
217. ICML 2026VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks
218. ICML 2026Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks
219. ICML 2026View Space: Learning Representation across Arbitrary Graphs
220. ICML 2026What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition
221. ICML 2026What Makes a Desired Graph for Relational Deep Learning?
222. ICML 2026When Do Graph Foundation Models Transfer? A Data-Centric Theory
223. ICML 2026Which Algorithms Can Graph Neural Networks Learn?
224. ICML 2026Why Are Linear RNNs More Parallelizable?
225. NeurIPS 2026A Differentiable 3D Scene Graph Metric via Contextual Hellinger Triplet Geometry
226. NeurIPS 2026A Graph Foundation Model for Unified Clustering
227. NeurIPS 2026A New Perspective on Target-Conditioned Structural Dynamics for Link Prediction in Dynamic Graphs
228. NeurIPS 2026A Topological Encoder Decoder Framework for Temporal Graph Learning
229. NeurIPS 2026A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion
230. NeurIPS 2026A2I: Adjacency-to-Image Structural Encodings for Graph Learning
231. NeurIPS 2026Adaptive Feature Propagation for Attribute-Missing Graph Clustering
232. NeurIPS 2026Adaptive Gated Simplicial Propagation for Node Classification in Multimodal Graphs
233. NeurIPS 2026Adaptive Multi-view Graph Contrastive Learning via Fractional Continuous Dynamics
234. NeurIPS 2026Aggregation Dispersion: An Information-Geometric Diagnostic of Oversmoothing in Graph Neural Networks
235. NeurIPS 2026Approximation of Maximally Monotone Operators : A Graph Convergence Perspective
236. NeurIPS 2026Avoiding Feature Collapse in Graph ODEs via Hysteretic Topology Evolution
237. NeurIPS 2026B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
238. NeurIPS 2026Benchmarking Graph Self-Supervised Learning for Node-Level Tasks: Insights and Strong Baseline
239. NeurIPS 2026Beyond Adjacent Layers: Graph-Guided Layer Fusion for Compressing Large Language Models
240. NeurIPS 2026Beyond Node Sequences: Relational Diffusion for Unified Graph Learning
241. NeurIPS 2026Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
242. NeurIPS 2026Biological Graph Priors Enable Representation Learning for Cellular Microscopy
243. NeurIPS 2026Boosting Graph Contrastive Learning via Manifold-Guided Representation Disentanglement
244. NeurIPS 2026Bridging Graph Worlds: Neural Approximation of Gromov-Wasserstein Distances
245. NeurIPS 2026Caterpillar GNN: Replacing Message Passing with Graph-Level Aggregation
246. NeurIPS 2026Chaining 2-FWL GNNs for Combinatorial Graph Alignment
247. NeurIPS 2026CHORD: Cross-Model Hallucination Detection via Relational Graph Discrimination
248. NeurIPS 2026CommunityKV: Efficient Long-Context Decoding via Graph Partitioning
249. NeurIPS 2026Contractive Monoids: The Algebra Behind Stable Residual Propagation in Deep Graph Neural Networks
250. NeurIPS 2026Contrastive Adversarial Training for Robust Graph Neural Networks under Label Poisoning

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.