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

489 papers, page 3 of 10

101. NeurIPS 2023Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems
102. NeurIPS 2023Deep Insights into Noisy Pseudo Labeling on Graph Data
103. NeurIPS 2023Deep learning with kernels through RKHM and the Perron-Frobenius operator
104. NeurIPS 2023Deep Patch Visual Odometry
105. NeurIPS 2023Deep Recurrent Optimal Stopping
106. NeurIPS 2023Deep Stochastic Processes via Functional Markov Transition Operators
107. NeurIPS 2023DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
108. NeurIPS 2023DeepPCR: Parallelizing Sequential Operations in Neural Networks
109. NeurIPS 2023Degraded Polygons Raise Fundamental Questions of Neural Network Perception
110. NeurIPS 2023Demystifying Oversmoothing in Attention-Based Graph Neural Networks
111. NeurIPS 2023Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
112. NeurIPS 2023Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer
113. NeurIPS 2023Differentiable and Stable Long-Range Tracking of Multiple Posterior Modes
114. NeurIPS 2023Differentiable sorting for censored time-to-event data.
115. NeurIPS 2023Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection
116. NeurIPS 2023Diffused Redundancy in Pre-trained Representations
117. NeurIPS 2023Diffusion Model for Graph Inverse Problems: Towards Effective Source Localization on Complex Networks
118. NeurIPS 2023Diffusion Probabilistic Models for Structured Node Classification
119. NeurIPS 2023Directional diffusion models for graph representation learning
120. NeurIPS 2023Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
121. NeurIPS 2023Distributed Personalized Empirical Risk Minimization
122. NeurIPS 2023Distributionally Robust Skeleton Learning of Discrete Bayesian Networks
123. NeurIPS 2023DiViNeT: 3D Reconstruction from Disparate Views using Neural Template Regularization
124. NeurIPS 2023Does a sparse ReLU network training problem always admit an optimum ?
125. NeurIPS 2023Does Graph Distillation See Like Vision Dataset Counterpart?
126. NeurIPS 2023Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
127. NeurIPS 2023Don’t just prune by magnitude! Your mask topology is a secret weapon
128. NeurIPS 2023Double Gumbel Q-Learning
129. NeurIPS 2023DSR: Dynamical Surface Representation as Implicit Neural Networks for Protein
130. NeurIPS 2023Dynamic Sparsity Is Channel-Level Sparsity Learner
131. NeurIPS 2023Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes
132. NeurIPS 2023Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies
133. NeurIPS 2023Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks
134. NeurIPS 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
135. NeurIPS 2023Effective Bayesian Heteroscedastic Regression with Deep Neural Networks
136. NeurIPS 2023Efficient Beam Tree Recursion
137. NeurIPS 2023Efficient Learning of Linear Graph Neural Networks via Node Subsampling
138. NeurIPS 2023Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization
139. NeurIPS 2023Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
140. NeurIPS 2023Efficiently incorporating quintuple interactions into geometric deep learning force fields
141. NeurIPS 2023EICIL: Joint Excitatory Inhibitory Cycle Iteration Learning for Deep Spiking Neural Networks
142. NeurIPS 2023Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity
143. NeurIPS 2023Empowering Convolutional Neural Nets with MetaSin Activation
144. NeurIPS 2023Encoding Human Behavior in Information Design through Deep Learning
145. NeurIPS 2023Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
146. NeurIPS 2023Enhancing Sharpness-Aware Optimization Through Variance Suppression
147. NeurIPS 2023Ensemble-based Deep Reinforcement Learning for Vehicle Routing Problems under Distribution Shift
148. NeurIPS 2023Entropic Neural Optimal Transport via Diffusion Processes
149. NeurIPS 2023Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs
150. NeurIPS 2023Episodic Multi-Task Learning with Heterogeneous Neural Processes

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.