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

842 papers, page 9 of 17

401. NeurIPS 2023ARTree: A Deep Autoregressive Model for Phylogenetic Inference
402. NeurIPS 2023ASPEN: Breaking Operator Barriers for Efficient Parallelization of Deep Neural Networks
403. NeurIPS 2023Asymmetric Certified Robustness via Feature-Convex Neural Networks
404. NeurIPS 2023Augmentation-Free Dense Contrastive Knowledge Distillation for Efficient Semantic Segmentation
405. NeurIPS 2023AutoGO: Automated Computation Graph Optimization for Neural Network Evolution
406. NeurIPS 2023Automatic Integration for Spatiotemporal Neural Point Processes
407. NeurIPS 2023Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
408. NeurIPS 2023Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks
409. NeurIPS 2023Beyond Exponential Graph: Communication-Efficient Topologies for Decentralized Learning via Finite-time Convergence
410. NeurIPS 2023Beyond Geometry: Comparing the Temporal Structure of Computation in Neural Circuits with Dynamical Similarity Analysis
411. NeurIPS 2023Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time
412. NeurIPS 2023Beyond probability partitions: Calibrating neural networks with semantic aware grouping
413. NeurIPS 2023Bifurcations and loss jumps in RNN training
414. NeurIPS 2023Binarized Neural Machine Translation
415. NeurIPS 2023Binary Radiance Fields
416. NeurIPS 2023Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training
417. NeurIPS 2023Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization
418. NeurIPS 2023Boosting Learning for LDPC Codes to Improve the Error-Floor Performance
419. NeurIPS 2023Boosting Verification of Deep Reinforcement Learning via Piece-Wise Linear Decision Neural Networks
420. NeurIPS 2023BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization
421. NeurIPS 2023Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing
422. NeurIPS 2023Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs
423. NeurIPS 2023CAT-Walk: Inductive Hypergraph Learning via Set Walks
424. NeurIPS 2023Causes and Effects of Unanticipated Numerical Deviations in Neural Network Inference Frameworks
425. NeurIPS 2023CBD: A Certified Backdoor Detector Based on Local Dominant Probability
426. NeurIPS 2023Certifiably Robust Graph Contrastive Learning
427. NeurIPS 2023Certification of Distributional Individual Fairness
428. NeurIPS 2023Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
429. NeurIPS 2023Cognitive Model Discovery via Disentangled RNNs
430. NeurIPS 2023Cognitive Steering in Deep Neural Networks via Long-Range Modulatory Feedback Connections
431. NeurIPS 2023Combating Bilateral Edge Noise for Robust Link Prediction
432. NeurIPS 2023Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
433. NeurIPS 2023Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
434. NeurIPS 2023Compression with Bayesian Implicit Neural Representations
435. NeurIPS 2023Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy
436. NeurIPS 2023Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data
437. NeurIPS 2023Conformal Prediction for Time Series with Modern Hopfield Networks
438. NeurIPS 2023Contextual Gaussian Process Bandits with Neural Networks
439. NeurIPS 2023Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
440. NeurIPS 2023Convergence of Actor-Critic with Multi-Layer Neural Networks
441. NeurIPS 2023Convolutional Neural Operators for robust and accurate learning of PDEs
442. NeurIPS 2023Coop: Memory is not a Commodity
443. NeurIPS 2023CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics
444. NeurIPS 2023Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry
445. NeurIPS 2023CosNet: A Generalized Spectral Kernel Network
446. NeurIPS 2023CP-SLAM: Collaborative Neural Point-based SLAM System
447. NeurIPS 2023Crystal Structure Prediction by Joint Equivariant Diffusion
448. NeurIPS 2023CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
449. NeurIPS 2023Curriculum Learning for Graph Neural Networks: Which Edges Should We Learn First
450. NeurIPS 2023Curvature Filtrations for Graph Generative Model Evaluation

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.