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 11 of 15

501. NeurIPS 2023Learning in the Presence of Low-dimensional Structure: A Spiked Random Matrix Perspective
502. NeurIPS 2023Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
503. NeurIPS 2023Learning Large Graph Property Prediction via Graph Segment Training
504. NeurIPS 2023Learning Large-scale Neural Fields via Context Pruned Meta-Learning
505. NeurIPS 2023Learning Layer-wise Equivariances Automatically using Gradients
506. NeurIPS 2023Learning Provably Robust Estimators for Inverse Problems via Jittering
507. NeurIPS 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecification
508. NeurIPS 2023Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction
509. NeurIPS 2023Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
510. NeurIPS 2023Learning threshold neurons via edge of stability
511. NeurIPS 2023Learning To Dive In Branch And Bound
512. NeurIPS 2023Learning Trajectories are Generalization Indicators
513. NeurIPS 2023Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
514. NeurIPS 2023Leveraging the two-timescale regime to demonstrate convergence of neural networks
515. NeurIPS 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference
516. NeurIPS 2023LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing
517. NeurIPS 2023Locality-Aware Generalizable Implicit Neural Representation
518. NeurIPS 2023LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees
519. NeurIPS 2023Long Sequence Hopfield Memory
520. NeurIPS 2023Lovász Principle for Unsupervised Graph Representation Learning
521. NeurIPS 2023Low Tensor Rank Learning of Neural Dynamics
522. NeurIPS 2023MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
523. NeurIPS 2023Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
524. NeurIPS 2023Max-Sliced Mutual Information
525. NeurIPS 2023Maximum Independent Set: Self-Training through Dynamic Programming
526. NeurIPS 2023MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy
527. NeurIPS 2023Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference
528. NeurIPS 2023Meta-Learning with Neural Bandit Scheduler
529. NeurIPS 2023MIMEx: Intrinsic Rewards from Masked Input Modeling
530. NeurIPS 2023Mitigating the Popularity Bias of Graph Collaborative Filtering: A Dimensional Collapse Perspective
531. NeurIPS 2023MLFMF: Data Sets for Machine Learning for Mathematical Formalization
532. NeurIPS 2023MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability
533. NeurIPS 2023Modulated Neural ODEs
534. NeurIPS 2023Module-wise Training of Neural Networks via the Minimizing Movement Scheme
535. NeurIPS 2023Multi-task Graph Neural Architecture Search with Task-aware Collaboration and Curriculum
536. NeurIPS 2023MultiMoDN—Multimodal, Multi-Task, Interpretable Modular Networks
537. NeurIPS 2023Multiplication-Free Transformer Training via Piecewise Affine Operations
538. NeurIPS 2023MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
539. NeurIPS 2023NAR-Former V2: Rethinking Transformer for Universal Neural Network Representation Learning
540. NeurIPS 2023Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives
541. NeurIPS 2023Nearly Tight Bounds For Differentially Private Multiway Cut
542. NeurIPS 2023NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks
543. NeurIPS 2023Network Regression with Graph Laplacians
544. NeurIPS 2023Networks are Slacking Off: Understanding Generalization Problem in Image Deraining
545. NeurIPS 2023Neural (Tangent Kernel) Collapse
546. NeurIPS 2023Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions
547. NeurIPS 2023Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization
548. NeurIPS 2023Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity
549. NeurIPS 2023Neural Functional Transformers
550. NeurIPS 2023Neural Graph Generation from Graph Statistics

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.