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 23 of 33

1101. ICML 2023Minimal Width for Universal Property of Deep RNN
1102. ICML 2023Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
1103. ICML 2023Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables
1104. ICML 2023Moccasin: Efficient Tensor Rematerialization for Neural Networks
1105. ICML 2023Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
1106. ICML 2023MODeL: Memory Optimizations for Deep Learning
1107. ICML 2023Multi-class Graph Clustering via Approximated Effective $p$-Resistance
1108. ICML 2023Neural networks trained with SGD learn distributions of increasing complexity
1109. ICML 2023Neural Wave Machines: Learning Spatiotemporally Structured Representations with Locally Coupled Oscillatory Recurrent Neural Networks
1110. ICML 2023NeuralSlice: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes
1111. ICML 2023Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
1112. ICML 2023On the Connection Between MPNN and Graph Transformer
1113. ICML 2023On the Expressive Power of Geometric Graph Neural Networks
1114. ICML 2023On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization
1115. ICML 2023On the Initialization of Graph Neural Networks
1116. ICML 2023Optimizing NOTEARS Objectives via Topological Swaps
1117. ICML 2023Overcoming Simplicity Bias in Deep Networks using a Feature Sieve
1118. ICML 2023Path Neural Networks: Expressive and Accurate Graph Neural Networks
1119. ICML 2023Personalized Subgraph Federated Learning
1120. ICML 2023Perturbation Analysis of Neural Collapse
1121. ICML 2023Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
1122. ICML 2023Quantitative Universal Approximation Bounds for Deep Belief Networks
1123. ICML 2023Randomized Schur Complement Views for Graph Contrastive Learning
1124. ICML 2023Relevant Walk Search for Explaining Graph Neural Networks
1125. ICML 2023Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
1126. ICML 2023Revisiting Structured Variational Autoencoders
1127. ICML 2023Revisiting Weighted Aggregation in Federated Learning with Neural Networks
1128. ICML 2023RGE: A Repulsive Graph Rectification for Node Classification via Influence
1129. ICML 2023RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations
1130. ICML 2023Scaling Spherical CNNs
1131. ICML 2023Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning
1132. ICML 2023SeedGNN: Graph Neural Network for Supervised Seeded Graph Matching
1133. ICML 2023SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning
1134. ICML 2023SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
1135. ICML 2023SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge
1136. ICML 2023Spatial Implicit Neural Representations for Global-Scale Species Mapping
1137. ICML 2023Special Properties of Gradient Descent with Large Learning Rates
1138. ICML 2023spred: Solving L1 Penalty with SGD
1139. ICML 2023Statistical Foundations of Prior-Data Fitted Networks
1140. ICML 2023StriderNet: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
1141. ICML 2023Subequivariant Graph Reinforcement Learning in 3D Environments
1142. ICML 2023Taming graph kernels with random features
1143. ICML 2023The Acquisition of Physical Knowledge in Generative Neural Networks
1144. ICML 2023The Hessian perspective into the Nature of Convolutional Neural Networks
1145. ICML 2023Total Variation Graph Neural Networks
1146. ICML 2023Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
1147. ICML 2023Towards Deep Attention in Graph Neural Networks: Problems and Remedies
1148. ICML 2023Towards Robust Graph Incremental Learning on Evolving Graphs
1149. ICML 2023Towards Understanding Generalization of Graph Neural Networks
1150. ICML 2023Trompt: Towards a Better Deep Neural Network for Tabular Data

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.