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

168 papers, page 3 of 4

101. ICML 2026MDGMIX: Boundary-Aware Subgraph Mixing for Multi-Domain Graph Pre-Training
102. ICML 2026Message Passing on the Edge: Towards Scalable and Expressive GNNs
103. ICML 2026Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective
104. ICML 2026MIMO-LP: A Multi-Input Multi-Output Framework for Subgraph-based Link Prediction
105. ICML 2026Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection
106. ICML 2026Multi-scale Explainer for Graph Neural Networks
107. ICML 2026MUSA-PINN: Multi-scale Weak-form Physics-Informed Neural Networks for Fluid Flow in Complex Geometries
108. ICML 2026MV-FGAD: Towards Efficient and Effective Federated Graph Anomaly Detection via Multi-view Learning
109. ICML 2026NetDiff: Graph Diffusion with Improved Global Capabilities to Generate and Update Mobile Network Topologies
110. ICML 2026Normality Calibration in Semi-supervised Graph Anomaly Detection
111. ICML 2026On the Expressive Power of GNNs to Solve Linear SDPs
112. ICML 2026On the Spectral Unreachability of Brain Graph Learning
113. ICML 2026One-Step Graph-Structured Neural Flows for Irregular Multivariate Time Series Classification
114. ICML 2026OpenMAG: A Comprehensive Benchmark for Multimodal-Attributed Graph
115. ICML 2026PhenoBrain: Phenotype-Conditioned Long-Range Communication for Multi-Modal Brain Network Analysis
116. ICML 2026Plain Transformers are Surprisingly Powerful Link Predictors
117. ICML 2026PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs
118. ICML 2026PSG-Nav: Probabilistic Scene Graph Navigation via Multiverse Decision Making
119. ICML 2026pTNAS: Progressive Neural Architecture Search for Tabular Data
120. ICML 2026Quantile-Free Uncertainty Quantification in Graph Neural Networks
121. ICML 2026Quantum Algorithms for Triangle Cut Sparsification
122. ICML 2026RADE: Random Add-Drop Edge as a Regularizer
123. ICML 2026RAPNet: Accelerating Algebraic Multigrid with Learned Sparse Corrections
124. ICML 2026Reconstruction Outcomes Look Similar but Processes Differ: Improving Context Consistency and Coverage in Graph Masked Auto-Encoder
125. ICML 2026Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases
126. ICML 2026Rethinking Contrastive Learning for Graph Collaborative Filtering: Limitations and a Simple Remedy
127. ICML 2026Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
128. ICML 2026Rethinking GNNs and Missing Features: Challenges, Evaluation and a Robust Solution
129. ICML 2026Rethinking Graph Transformers as Graph Signal Denoisers: The Role of Block-Diagonal Priors
130. ICML 2026Rethinking Parameter Sharing as Graph Coloring for Structured Compression
131. ICML 2026Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
132. ICML 2026Revisiting Pre-Propagation GNNs: Robust Diffusion Operators and Hidden-State Re-Propagation
133. ICML 2026RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs
134. ICML 2026S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
135. ICML 2026Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
136. ICML 2026SC-FAGC: Size Constrained Fast Anchor-based Graph Clustering
137. ICML 2026Scalable Topology-Preserving Graph Coarsening: Concepts and Algorithms
138. ICML 2026SFCLTA: Spectral Fusion Contrastive Learning with Topology-Adaptive Graph Augmentation
139. ICML 2026Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
140. ICML 2026SI-IGCL: Subject Invariance-aware Inverse Graph Contrastive Learning for Psychiatric Disorder Identification
141. ICML 2026SimGFM: Simplifying Discrete Flow Matching for Graph Generation
142. ICML 2026Size Transferability of Graph Convolutional Networks across Sparsity: A Generalized Graphon Perspective
143. ICML 2026Smoothness Errors in Dynamics Models and How to Avoid Them
144. ICML 2026SpaEF: Spatially Resolved Transcriptomics Data Element-Wise Denoising Framework Powered by Large Models
145. ICML 2026Sparse Bayesian Deep Functional Learning with Structured Region Selection
146. ICML 2026Spike-HTR: Spiking Neural Transformer for Handwritten Text Recognition
147. ICML 2026ST-TGExplainer: Disentangling Stability and Transition Patterns for Temporal GNN Interpretability
148. ICML 2026Structure-Centric Graph Foundation Model via Geometric Bases
149. ICML 2026Subspace-Aware Feature Reshaping for Open-Set Graph Class-Incremental Learning
150. ICML 2026SuperHype: Hypergraph Generation via Graph-Superposition Decomposition

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.