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

297 papers, page 6 of 6

251. NeurIPS 2024On the Scalability of GNNs for Molecular Graphs
252. NeurIPS 2024Online Relational Inference for Evolving Multi-agent Interacting Systems
253. NeurIPS 2024Open-Book Neural Algorithmic Reasoning
254. NeurIPS 2024Optimal Algorithms for Learning Partitions with Faulty Oracles
255. NeurIPS 2024Probabilistic Graph Rewiring via Virtual Nodes
256. NeurIPS 2024Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
257. NeurIPS 2024ProG: A Graph Prompt Learning Benchmark
258. NeurIPS 2024Pure Message Passing Can Estimate Common Neighbor for Link Prediction
259. NeurIPS 2024RAGraph: A General Retrieval-Augmented Graph Learning Framework
260. NeurIPS 2024Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting Approach
261. NeurIPS 2024Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations
262. NeurIPS 2024Rethinking Reconstruction-based Graph-Level Anomaly Detection: Limitations and a Simple Remedy
263. NeurIPS 2024Rethinking the Capacity of Graph Neural Networks for Branching Strategy
264. NeurIPS 2024Revisiting Score Propagation in Graph Out-of-Distribution Detection
265. NeurIPS 2024Revisiting Self-Supervised Heterogeneous Graph Learning from Spectral Clustering Perspective
266. NeurIPS 2024Robust Graph Neural Networks via Unbiased Aggregation
267. NeurIPS 2024Scale Equivariant Graph Metanetworks
268. NeurIPS 2024Schur Nets: exploiting local structure for equivariance in higher order graph neural networks
269. NeurIPS 2024Sequential Signal Mixing Aggregation for Message Passing Graph Neural Networks
270. NeurIPS 2024Similarity-Navigated Conformal Prediction for Graph Neural Networks
271. NeurIPS 2024Spatio-Spectral Graph Neural Networks
272. NeurIPS 2024SpeAr: A Spectral Approach for Zero-Shot Node Classification
273. NeurIPS 2024Spectral Graph Pruning Against Over-Squashing and Over-Smoothing
274. NeurIPS 2024Spiking Graph Neural Network on Riemannian Manifolds
275. NeurIPS 2024State Space Models on Temporal Graphs: A First-Principles Study
276. NeurIPS 2024Stochastic contextual bandits with graph feedback: from independence number to MAS number
277. NeurIPS 2024Supra-Laplacian Encoding for Transformer on Dynamic Graphs
278. NeurIPS 2024Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed
279. NeurIPS 2024TFGDA: Exploring Topology and Feature Alignment in Semi-supervised Graph Domain Adaptation through Robust Clustering
280. NeurIPS 2024The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks
281. NeurIPS 2024Towards Dynamic Message Passing on Graphs
282. NeurIPS 2024Towards Principled Graph Transformers
283. NeurIPS 2024Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling Network
284. NeurIPS 2024Transformers need glasses! Information over-squashing in language tasks
285. NeurIPS 2024UGC: Universal Graph Coarsening
286. NeurIPS 2024Uncovering the Redundancy in Graph Self-supervised Learning Models
287. NeurIPS 2024Unified Graph Augmentations for Generalized Contrastive Learning on Graphs
288. NeurIPS 2024Unifying Generation and Prediction on Graphs with Latent Graph Diffusion
289. NeurIPS 2024Unifying Homophily and Heterophily for Spectral Graph Neural Networks via Triple Filter Ensembles
290. NeurIPS 2024UniGAD: Unifying Multi-level Graph Anomaly Detection
291. NeurIPS 2024Unitary Convolutions for Learning on Graphs and Groups
292. NeurIPS 2024Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs
293. NeurIPS 2024Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning
294. NeurIPS 2024What do Graph Neural Networks learn? Insights from Tropical Geometry
295. NeurIPS 2024What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks
296. NeurIPS 2024What Matters in Graph Class Incremental Learning? An Information Preservation Perspective
297. NeurIPS 2024Why the Metric Backbone Preserves Community Structure

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.