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

1,648 papers, page 27 of 33

1301. NeurIPS 2023Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity
1302. NeurIPS 2023Empowering Convolutional Neural Nets with MetaSin Activation
1303. NeurIPS 2023Encoding Human Behavior in Information Design through Deep Learning
1304. NeurIPS 2023Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
1305. NeurIPS 2023Enhancing Sharpness-Aware Optimization Through Variance Suppression
1306. NeurIPS 2023Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift
1307. NeurIPS 2023Entropic Neural Optimal Transport via Diffusion Processes
1308. NeurIPS 2023Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs
1309. NeurIPS 2023Episodic Multi-Task Learning with Heterogeneous Neural Processes
1310. NeurIPS 2023Epistemic Neural Networks
1311. NeurIPS 2023Equivariant Neural Operator Learning with Graphon Convolution
1312. NeurIPS 2023Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics
1313. NeurIPS 2023ESSEN: Improving Evolution State Estimation for Temporal Networks using Von Neumann Entropy
1314. NeurIPS 2023Euler-Lagrange Analysis of Generative Adversarial Networks
1315. NeurIPS 2023Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
1316. NeurIPS 2023Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis
1317. NeurIPS 2023Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance
1318. NeurIPS 2023Evolving Connectivity for Recurrent Spiking Neural Networks
1319. NeurIPS 2023Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
1320. NeurIPS 2023Exact Verification of ReLU Neural Control Barrier Functions
1321. NeurIPS 2023Explain Any Concept: Segment Anything Meets Concept-Based Explanation
1322. NeurIPS 2023Explainable Brain Age Prediction using coVariance Neural Networks
1323. NeurIPS 2023Explaining V1 Properties with a Biologically Constrained Deep Learning Architecture
1324. NeurIPS 2023Exploring Loss Functions for Time-based Training Strategy in Spiking Neural Networks
1325. NeurIPS 2023Expressive probabilistic sampling in recurrent neural networks
1326. NeurIPS 2023Expressive Sign Equivariant Networks for Spectral Geometric Learning
1327. NeurIPS 2023Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
1328. NeurIPS 2023Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes
1329. NeurIPS 2023Fair Graph Distillation
1330. NeurIPS 2023Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
1331. NeurIPS 2023Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
1332. NeurIPS 2023Federated Learning via Meta-Variational Dropout
1333. NeurIPS 2023FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
1334. NeurIPS 2023Fine-grained Expressivity of Graph Neural Networks
1335. NeurIPS 2023Fixing the NTK: From Neural Network Linearizations to Exact Convex Programs
1336. NeurIPS 2023Flat Seeking Bayesian Neural Networks
1337. NeurIPS 2023FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow
1338. NeurIPS 2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
1339. NeurIPS 2023FourierHandFlow: Neural 4D Hand Representation Using Fourier Query Flow
1340. NeurIPS 2023Fragment-based Pretraining and Finetuning on Molecular Graphs
1341. NeurIPS 2023From Tempered to Benign Overfitting in ReLU Neural Networks
1342. NeurIPS 2023Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
1343. NeurIPS 2023Full-Atom Protein Pocket Design via Iterative Refinement
1344. NeurIPS 2023Function Space Bayesian Pseudocoreset for Bayesian Neural Networks
1345. NeurIPS 2023Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks
1346. NeurIPS 2023Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
1347. NeurIPS 2023GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection
1348. NeurIPS 2023GALOPA: Graph Transport Learning with Optimal Plan Alignment
1349. NeurIPS 2023Generalised f-Mean Aggregation for Graph Neural Networks
1350. NeurIPS 2023Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations

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.