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

141 papers, page 1 of 3

1. ICML 2025A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
2. ICML 2025A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Attention Networks
3. ICML 2025A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
4. ICML 2025A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
5. ICML 2025A Recipe for Causal Graph Regression: Confounding Effects Revisited
6. ICML 2025Adapting Precomputed Features for Efficient Graph Condensation
7. ICML 2025Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching
8. ICML 2025Adversarial Combinatorial Semi-bandits with Graph Feedback
9. ICML 2025Aggregation Buffer: Revisiting DropEdge with a New Parameter Block
10. ICML 2025Analytical Construction on Geometric Architectures: Transitioning from Static to Temporal Link Prediction
11. ICML 2025Attention Mechanisms Perspective: Exploring LLM Processing of Graph-Structured Data
12. ICML 2025AutoGFM: Automated Graph Foundation Model with Adaptive Architecture Customization
13. ICML 2025Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
14. ICML 2025Best of Both Worlds: Advantages of Hybrid Graph Sequence Models
15. ICML 2025Beyond Message Passing: Neural Graph Pattern Machine
16. ICML 2025Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective
17. ICML 2025Boosting Protein Graph Representations through Static-Dynamic Fusion
18. ICML 2025Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
19. ICML 2025CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition
20. ICML 2025Commute Graph Neural Networks
21. ICML 2025COMRECGC: Global Graph Counterfactual Explainer through Common Recourse
22. ICML 2025Cooperation of Experts: Fusing Heterogeneous Information with Large Margin
23. ICML 2025Covered Forest: Fine-grained generalization analysis of graph neural networks
24. ICML 2025CSG-ODE: ControlSynth Graph ODE For Modeling Complex Evolution of Dynamic Graphs
25. ICML 2025CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection
26. ICML 2025De-coupled NeuroGF for Shortest Path Distance Approximations on Large Terrain Graphs
27. ICML 2025Delay-DSGN: A Dynamic Spiking Graph Neural Network with Delay Mechanisms for Evolving Graph
28. ICML 2025Directed Graph Grammars for Sequence-based Learning
29. ICML 2025Discrete Neural Algorithmic Reasoning
30. ICML 2025Disentangled Graph Spectral Domain Adaptation
31. ICML 2025Disentangling Invariant Subgraph via Variance Contrastive Estimation under Distribution Shifts
32. ICML 2025Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic Transforms
33. ICML 2025Do We Really Need Message Passing in Brain Network Modeling?
34. ICML 2025Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis
35. ICML 2025Dynamic Similarity Graph Construction with Kernel Density Estimation
36. ICML 2025EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification
37. ICML 2025EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction
38. ICML 2025Efficient Graph Continual Learning via Lightweight Graph Neural Tangent Kernels-based Dataset Distillation
39. ICML 2025EmoGrowth: Incremental Multi-label Emotion Decoding with Augmented Emotional Relation Graph
40. ICML 2025ENAHPool: The Edge-Node Attention-based Hierarchical Pooling for Graph Neural Networks
41. ICML 2025Enhancing Graph Contrastive Learning for Protein Graphs from Perspective of Invariance
42. ICML 2025Enhancing Graph Invariant Learning from a Negative Inference Perspective
43. ICML 2025Enhancing Spectral GNNs: From Topology and Perturbation Perspectives
44. ICML 2025Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks
45. ICML 2025Equivalence is All: A Unified View for Self-supervised Graph Learning
46. ICML 2025EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
47. ICML 2025Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
48. ICML 2025FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks
49. ICML 2025From Theory to Practice: Rethinking Green and Martin Kernels for Unleashing Graph Transformers
50. ICML 2025G-Adaptivity: optimised graph-based mesh relocation for finite element methods

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.