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

533 papers, page 7 of 11

301. ICML 2025Towards characterizing the value of edge embeddings in Graph Neural Networks
302. ICML 2025Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory
303. ICML 2025Unifews: You Need Fewer Operations for Efficient Graph Neural Networks
304. ICML 2025Vision Graph Prompting via Semantic Low-Rank Decomposition
305. ICML 2025Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win
306. ICML 2025What Makes a Good Feedforward Computational Graph?
307. ICML 2025When Do LLMs Help With Node Classification? A Comprehensive Analysis
308. ICML 2025WILTing Trees: Interpreting the Distance Between MPNN Embeddings
309. ICML 2025Zero-Shot Generalization of GNNs over Distinct Attribute Domains
310. ICML 2024A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
311. ICML 2024A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering
312. ICML 2024An Efficient Maximal Ancestral Graph Listing Algorithm
313. ICML 2024CKGConv: General Graph Convolution with Continuous Kernels
314. ICML 2024Class-Imbalanced Graph Learning without Class Rebalancing
315. ICML 2024Collective Certified Robustness against Graph Injection Attacks
316. ICML 2024Community-Invariant Graph Contrastive Learning
317. ICML 2024Comparing Graph Transformers via Positional Encodings
318. ICML 2024Cooperative Graph Neural Networks
319. ICML 2024Disentangled Graph Self-supervised Learning for Out-of-Distribution Generalization
320. ICML 2024Editing Partially Observable Networks via Graph Diffusion Models
321. ICML 2024Effective Federated Graph Matching
322. ICML 2024Empowering Graph Invariance Learning with Deep Spurious Infomax
323. ICML 2024Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
324. ICML 2024EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction
325. ICML 2024Explaining Graph Neural Networks via Structure-aware Interaction Index
326. ICML 2024Generalization Error of Graph Neural Networks in the Mean-field Regime
327. ICML 2024Generalized Sobolev Transport for Probability Measures on a Graph
328. ICML 2024Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
329. ICML 2024Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
330. ICML 2024Graph Adversarial Diffusion Convolution
331. ICML 2024Graph As Point Set
332. ICML 2024Graph Automorphism Group Equivariant Neural Networks
333. ICML 2024Graph Distillation with Eigenbasis Matching
334. ICML 2024Graph External Attention Enhanced Transformer
335. ICML 2024Graph Geometry-Preserving Autoencoders
336. ICML 2024Graph Mixup on Approximate Gromov–Wasserstein Geodesics
337. ICML 2024Graph Neural Network Explanations are Fragile
338. ICML 2024Graph Neural Networks Use Graphs When They Shouldn't
339. ICML 2024Graph Neural Networks with a Distribution of Parametrized Graphs
340. ICML 2024Graph Neural Stochastic Diffusion for Estimating Uncertainty in Node Classification
341. ICML 2024Graph Out-of-Distribution Detection Goes Neighborhood Shaping
342. ICML 2024Graph Structure Extrapolation for Out-of-Distribution Generalization
343. ICML 2024Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
344. ICML 2024Graph-Triggered Rising Bandits
345. ICML 2024HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
346. ICML 2024HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
347. ICML 2024Homomorphism Counts for Graph Neural Networks: All About That Basis
348. ICML 2024How Graph Neural Networks Learn: Lessons from Training Dynamics
349. ICML 2024How Interpretable Are Interpretable Graph Neural Networks?
350. ICML 2024Hypergraph-enhanced Dual Semi-supervised Graph Classification

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.