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 6 of 15

251. NeurIPS 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
252. NeurIPS 2023A graphon-signal analysis of graph neural networks
253. NeurIPS 2023A Metadata-Driven Approach to Understand Graph Neural Networks
254. NeurIPS 2023A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
255. NeurIPS 2023A new perspective on building efficient and expressive 3D equivariant graph neural networks
256. NeurIPS 2023A polar prediction model for learning to represent visual transformations
257. NeurIPS 2023A Recurrent Neural Circuit Mechanism of Temporal-scaling Equivariant Representation
258. NeurIPS 2023A Scalable Neural Network for DSIC Affine Maximizer Auction Design
259. NeurIPS 2023A Sublinear-Time Spectral Clustering Oracle with Improved Preprocessing Time
260. NeurIPS 2023A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
261. NeurIPS 2023Accelerated On-Device Forward Neural Network Training with Module-Wise Descending Asynchronism
262. NeurIPS 2023Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation
263. NeurIPS 2023Accelerating Molecular Graph Neural Networks via Knowledge Distillation
264. NeurIPS 2023Accurate Interpolation for Scattered Data through Hierarchical Residual Refinement
265. NeurIPS 2023Active Learning-Based Species Range Estimation
266. NeurIPS 2023Active Negative Loss Functions for Learning with Noisy Labels
267. NeurIPS 2023Adapting Neural Link Predictors for Data-Efficient Complex Query Answering
268. NeurIPS 2023Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds
269. NeurIPS 2023Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations
270. NeurIPS 2023Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
271. NeurIPS 2023Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
272. NeurIPS 2023Affinity-Aware Graph Networks
273. NeurIPS 2023AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
274. NeurIPS 2023Aiming towards the minimizers: fast convergence of SGD for overparametrized problems
275. NeurIPS 2023AMAG: Additive, Multiplicative and Adaptive Graph Neural Network For Forecasting Neuron Activity
276. NeurIPS 2023An Empirical Study Towards Prompt-Tuning for Graph Contrastive Pre-Training in Recommendations
277. NeurIPS 2023An Inductive Bias for Tabular Deep Learning
278. NeurIPS 2023An Information Theory Perspective on Variance-Invariance-Covariance Regularization
279. NeurIPS 2023Analysis of Variance of Multiple Causal Networks
280. NeurIPS 2023Analyzing Generalization of Neural Networks through Loss Path Kernels
281. NeurIPS 2023Anchor Data Augmentation
282. NeurIPS 2023Approximately Equivariant Graph Networks
283. NeurIPS 2023Approximation-Generalization Trade-offs under (Approximate) Group Equivariance
284. NeurIPS 2023Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning
285. NeurIPS 2023Are aligned neural networks adversarially aligned?
286. NeurIPS 2023Are GATs Out of Balance?
287. NeurIPS 2023ARTree: A Deep Autoregressive Model for Phylogenetic Inference
288. NeurIPS 2023ASPEN: Breaking Operator Barriers for Efficient Parallelization of Deep Neural Networks
289. NeurIPS 2023Asymmetric Certified Robustness via Feature-Convex Neural Networks
290. NeurIPS 2023Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation
291. NeurIPS 2023AutoGO: Automated Computation Graph Optimization for Neural Network Evolution
292. NeurIPS 2023Automatic Integration for Spatiotemporal Neural Point Processes
293. NeurIPS 2023Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
294. NeurIPS 2023Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks
295. NeurIPS 2023Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence
296. NeurIPS 2023Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis
297. NeurIPS 2023Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time
298. NeurIPS 2023Beyond probability partitions: Calibrating neural networks with semantic aware grouping
299. NeurIPS 2023Bifurcations and loss jumps in RNN training
300. NeurIPS 2023Binarized Neural Machine Translation

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.