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

201 papers, page 3 of 5

101. ICLR 2024Graph Transformers on EHRs: Better Representation Improves Downstream Performance
102. ICLR 2024GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks
103. ICLR 2024GraphPulse: Topological representations for temporal graph property prediction
104. ICLR 2024GTMGC: Using Graph Transformer to Predict Molecule’s Ground-State Conformation
105. ICLR 2024HiGen: Hierarchical Graph Generative Networks
106. ICLR 2024Hybrid Directional Graph Neural Network for Molecules
107. ICLR 2024iGraphMix: Input Graph Mixup Method for Node Classification
108. ICLR 2024InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior
109. ICLR 2024Learning Adaptive Multiresolution Transforms via Meta-Framelet-based Graph Convolutional Network
110. ICLR 2024Learning to solve Class-Constrained Bin Packing Problems via Encoder-Decoder Model
111. ICLR 2024Local Graph Clustering with Noisy Labels
112. ICLR 2024Locality-Aware Graph Rewiring in GNNs
113. ICLR 2024MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy
114. ICLR 2024Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
115. ICLR 2024One For All: Towards Training One Graph Model For All Classification Tasks
116. ICLR 2024Orbit-Equivariant Graph Neural Networks
117. ICLR 2024PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters
118. ICLR 2024Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
119. ICLR 2024PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks
120. ICLR 2024Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks
121. ICLR 2024Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
122. ICLR 2024Robust Angular Synchronization via Directed Graph Neural Networks
123. ICLR 2024SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
124. ICLR 2024StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning
125. ICLR 2024TEDDY: Trimming Edges with Degree-based Discrimination Strategy
126. ICLR 2024Temporal Generalization Estimation in Evolving Graphs
127. ICLR 2024Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
128. ICLR 2024Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
129. ICLR 2024Training Graph Transformers via Curriculum-Enhanced Attention Distillation
130. ICLR 2024Uncertainty-aware Graph-based Hyperspectral Image Classification
131. ICLR 2024Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
132. ICLR 2024Variance-enlarged Poisson Learning for Graph-based Semi-Supervised Learning with Extremely Sparse Labeled Data
133. ICLR 2024VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
134. ICLR 2023$\mathscr{N}$-WL: A New Hierarchy of Expressivity for Graph Neural Networks
135. ICLR 2023$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks
136. ICLR 2023A Convergent Single-Loop Algorithm for Relaxation of Gromov-Wasserstein in Graph Data
137. ICLR 2023A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps
138. ICLR 2023A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks
139. ICLR 2023Agent-based Graph Neural Networks
140. ICLR 2023Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
141. ICLR 2023Are More Layers Beneficial to Graph Transformers?
142. ICLR 2023AutoGT: Automated Graph Transformer Architecture Search
143. ICLR 2023Automated Data Augmentations for Graph Classification
144. ICLR 2023AutoTransfer: AutoML with Knowledge Transfer - An Application to Graph Neural Networks
145. ICLR 2023Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs
146. ICLR 2023Characterizing the Influence of Graph Elements
147. ICLR 2023Cycle to Clique (Cy2C) Graph Neural Network: A Sight to See beyond Neighborhood Aggregation
148. ICLR 2023DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks
149. ICLR 2023DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion
150. ICLR 2023Do We Really Need Complicated Model Architectures For Temporal Networks?

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.