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

51 papers, page 1 of 2

1. ICLR 2024A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
2. ICLR 2024BroGNet: Momentum-Conserving Graph Neural Stochastic Differential Equation for Learning Brownian Dynamics
3. ICLR 2024Clifford Group Equivariant Simplicial Message Passing Networks
4. ICLR 2024Conformal Inductive Graph Neural Networks
5. ICLR 2024Contrastive Learning is Spectral Clustering on Similarity Graph
6. ICLR 2024CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs
7. ICLR 2024Counting Graph Substructures with Graph Neural Networks
8. ICLR 2024Deep Temporal Graph Clustering
9. ICLR 2024Distinguished In Uniform: Self-Attention Vs. Virtual Nodes
10. ICLR 2024DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption
11. ICLR 2024Effective Structural Encodings via Local Curvature Profiles
12. ICLR 2024Forward Learning of Graph Neural Networks
13. ICLR 2024From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
14. ICLR 2024GNNCert: Deterministic Certification of Graph Neural Networks against Adversarial Perturbations
15. ICLR 2024Graph Lottery Ticket Automated
16. ICLR 2024Graph Metanetworks for Processing Diverse Neural Architectures
17. ICLR 2024Graph Neural Networks for Learning Equivariant Representations of Neural Networks
18. ICLR 2024Graph Parsing Networks
19. ICLR 2024Graph Transformers on EHRs: Better Representation Improves Downstream Performance
20. ICLR 2024GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks
21. ICLR 2024GraphPulse: Topological representations for temporal graph property prediction
22. ICLR 2024GTMGC: Using Graph Transformer to Predict Molecule’s Ground-State Conformation
23. ICLR 2024HiGen: Hierarchical Graph Generative Networks
24. ICLR 2024Hybrid Directional Graph Neural Network for Molecules
25. ICLR 2024iGraphMix: Input Graph Mixup Method for Node Classification
26. ICLR 2024InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior
27. ICLR 2024Learning Adaptive Multiresolution Transforms via Meta-Framelet-based Graph Convolutional Network
28. ICLR 2024Learning to solve Class-Constrained Bin Packing Problems via Encoder-Decoder Model
29. ICLR 2024Local Graph Clustering with Noisy Labels
30. ICLR 2024Locality-Aware Graph Rewiring in GNNs
31. ICLR 2024MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy
32. ICLR 2024Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
33. ICLR 2024One For All: Towards Training One Graph Model For All Classification Tasks
34. ICLR 2024Orbit-Equivariant Graph Neural Networks
35. ICLR 2024PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters
36. ICLR 2024Polynormer: Polynomial-Expressive Graph Transformer in Linear Time
37. ICLR 2024PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks
38. ICLR 2024Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks
39. ICLR 2024Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
40. ICLR 2024Robust Angular Synchronization via Directed Graph Neural Networks
41. ICLR 2024SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases
42. ICLR 2024StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning
43. ICLR 2024TEDDY: Trimming Edges with Degree-based Discrimination Strategy
44. ICLR 2024Temporal Generalization Estimation in Evolving Graphs
45. ICLR 2024Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks
46. ICLR 2024Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks
47. ICLR 2024Training Graph Transformers via Curriculum-Enhanced Attention Distillation
48. ICLR 2024Uncertainty-aware Graph-based Hyperspectral Image Classification
49. ICLR 2024Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
50. ICLR 2024Variance-enlarged Poisson Learning for Graph-based Semi-Supervised Learning with Extremely Sparse Labeled 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.