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 9 of 15

401. NeurIPS 2023Explain Any Concept: Segment Anything Meets Concept-Based Explanation
402. NeurIPS 2023Explainable Brain Age Prediction using coVariance Neural Networks
403. NeurIPS 2023Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
404. NeurIPS 2023Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
405. NeurIPS 2023Expressive probabilistic sampling in recurrent neural networks
406. NeurIPS 2023Expressive Sign Equivariant Networks for Spectral Geometric Learning
407. NeurIPS 2023Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
408. NeurIPS 2023Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
409. NeurIPS 2023Fair Graph Distillation
410. NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
411. NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
412. NeurIPS 2023Federated Learning via Meta-Variational Dropout
413. NeurIPS 2023FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
414. NeurIPS 2023Fine-grained Expressivity of Graph Neural Networks
415. NeurIPS 2023Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs
416. NeurIPS 2023Flat Seeking Bayesian Neural Networks
417. NeurIPS 2023FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow
418. NeurIPS 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
419. NeurIPS 2023FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow
420. NeurIPS 2023Fragment-based Pretraining and Finetuning on Molecular Graphs
421. NeurIPS 2023From Tempered to Benign Overfitting in ReLU Neural Networks
422. NeurIPS 2023Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
423. NeurIPS 2023Full-Atom Protein Pocket Design via Iterative Refinement
424. NeurIPS 2023Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
425. NeurIPS 2023Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
426. NeurIPS 2023Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
427. NeurIPS 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
428. NeurIPS 2023GALOPA: Graph Transport Learning with Optimal Plan Alignment
429. NeurIPS 2023Generalised f-Mean Aggregation for Graph Neural Networks
430. NeurIPS 2023Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
431. NeurIPS 2023Generalization bounds for neural ordinary differential equations and deep residual networks
432. NeurIPS 2023Generating Behaviorally Diverse Policies with Latent Diffusion Models
433. NeurIPS 2023Geometry-Informed Neural Operator for Large-Scale 3D PDEs
434. NeurIPS 2023GeoTMI: Predicting Quantum Chemical Property with Easy-to-Obtain Geometry via Positional Denoising
435. NeurIPS 2023GEQ: Gaussian Kernel Inspired Equilibrium Models
436. NeurIPS 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
437. NeurIPS 2023GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
438. NeurIPS 2023Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization
439. NeurIPS 2023Globally injective and bijective neural operators
440. NeurIPS 2023GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
441. NeurIPS 2023Goal-conditioned Offline Planning from Curious Exploration
442. NeurIPS 2023Going beyond persistent homology using persistent homology
443. NeurIPS 2023GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks
444. NeurIPS 2023Gradient-Based Feature Learning under Structured Data
445. NeurIPS 2023Graph Clustering with Graph Neural Networks
446. NeurIPS 2023Graph Contrastive Learning with Stable and Scalable Spectral Encoding
447. NeurIPS 2023Graph Convolutional Kernel Machine versus Graph Convolutional Networks
448. NeurIPS 2023Graph Denoising Diffusion for Inverse Protein Folding
449. NeurIPS 2023Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
450. NeurIPS 2023Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis

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.