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

533 papers, page 5 of 11

201. ICML 2025Do We Really Need Message Passing in Brain Network Modeling?
202. ICML 2025Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis
203. ICML 2025Dynamic Similarity Graph Construction with Kernel Density Estimation
204. ICML 2025EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification
205. ICML 2025EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction
206. ICML 2025Efficient Graph Continual Learning via Lightweight Graph Neural Tangent Kernels-based Dataset Distillation
207. ICML 2025EmoGrowth: Incremental Multi-label Emotion Decoding with Augmented Emotional Relation Graph
208. ICML 2025ENAHPool: The Edge-Node Attention-based Hierarchical Pooling for Graph Neural Networks
209. ICML 2025Enhancing Graph Contrastive Learning for Protein Graphs from Perspective of Invariance
210. ICML 2025Enhancing Graph Invariant Learning from a Negative Inference Perspective
211. ICML 2025Enhancing Spectral GNNs: From Topology and Perturbation Perspectives
212. ICML 2025Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks
213. ICML 2025Equivalence is All: A Unified View for Self-supervised Graph Learning
214. ICML 2025EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
215. ICML 2025Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
216. ICML 2025FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
217. ICML 2025From Theory to Practice: Rethinking Green and Martin Kernels for Unleashing Graph Transformers
218. ICML 2025G-Adaptivity: optimised graph-based mesh relocation for finite element methods
219. ICML 2025G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
220. ICML 2025GCAL: Adapting Graph Models to Evolving Domain Shifts
221. ICML 2025Geometric Hyena Networks for Large-scale Equivariant Learning
222. ICML 2025GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning
223. ICML 2025Graph Adaptive Autoregressive Moving Average Models
224. ICML 2025Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models
225. ICML 2025Graph Generative Pre-trained Transformer
226. ICML 2025Graph Neural Network Generalization With Gaussian Mixture Model Based Augmentation
227. ICML 2025Graph-Assisted Stitching for Offline Hierarchical Reinforcement Learning
228. ICML 2025Graph-Based Algorithms for Diverse Similarity Search
229. ICML 2025Graph4MM: Weaving Multimodal Learning with Structural Information
230. ICML 2025GraphGPT: Generative Pre-trained Graph Eulerian Transformer
231. ICML 2025Griffin: Towards a Graph-Centric Relational Database Foundation Model
232. ICML 2025GrokFormer: Graph Fourier Kolmogorov-Arnold Transformers
233. ICML 2025Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks
234. ICML 2025Hgformer: Hyperbolic Graph Transformer for Collaborative Filtering
235. ICML 2025HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport
236. ICML 2025Hierarchical Graph Tokenization for Molecule-Language Alignment
237. ICML 2025How Expressive are Knowledge Graph Foundation Models?
238. ICML 2025How Much Can Transfer? BRIDGE: Bounded Multi-Domain Graph Foundation Model with Generalization Guarantees
239. ICML 2025Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic Partial Differential Equations
240. ICML 2025HyperNear: Unnoticeable Node Injection Attacks on Hypergraph Neural Networks
241. ICML 2025Implicit degree bias in the link prediction task
242. ICML 2025Implicit Subgraph Neural Network
243. ICML 2025Improved Approximations for Hard Graph Problems using Predictions
244. ICML 2025Improved Expressivity of Hypergraph Neural Networks through High-Dimensional Generalized Weisfeiler-Leman Algorithms
245. ICML 2025Improving Soft Unification with Knowledge Graph Embedding Methods
246. ICML 2025Improving the Effective Receptive Field of Message-Passing Neural Networks
247. ICML 2025iN2V: Bringing Transductive Node Embeddings to Inductive Graphs
248. ICML 2025Instance Correlation Graph-based Naive Bayes
249. ICML 2025Large Language-Geometry Model: When LLM meets Equivariance
250. ICML 2025Learn Beneficial Noise as Graph Augmentation

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.