Advances in Deep Learning for Graph Neural Networks
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 March 2026 | Viewed by 10
Special Issue Editors
Interests: machine learning; recommender systems; deep learning; graph neural networks
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; affective computing; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid progress of deep learning, Graph Neural Networks (GNNs) have become a cornerstone for modeling structured and relational data across numerous domains. Their success spans from recommender systems and social networks to molecular biology, traffic forecasting, and financial risk modeling. As GNNs continue to evolve, new challenges and opportunities arise in architecture design, scalability, interpretability, and real-world deployment.
This Special Issue aims to present the latest developments in deep learning techniques for GNNs, offering a platform for researchers, engineers, and practitioners to share innovative ideas, theoretical advancements, and impactful applications. We welcome contributions from both academia and industry that explore foundational models, optimization strategies, and domain-specific implementations.
Topics of interest include, but are not limited to, the following:
- Graph contrastive learning and self-supervised learning;
- Graph transformers and attention-based models;
- Spatio-temporal and dynamic GNNs;
- GNNs for recommendation and information retrieval;
- Graph generative and probabilistic models;
- Multimodal and heterogeneous graph learning;
- Explainability, fairness, and robustness in GNNs;
- Privacy-preserving and federated graph learning;
- Hardware-aware and efficient GNN implementations;
- GNNs in biomedicine, neuroscience, finance, cybersecurity, and other applications.
We look forward to your valuable submissions and to highlighting emerging research that advances both the theoretical foundations and practical impact of graph-based deep learning.
Dr. Yonghong Yu
Prof. Dr. Li Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- graph neural networks
- graph generative models
- multimodal graph learning
- recommendation systems
- privacy-preserving graph learning
- LLM-based graph learning
- self-supervised graph learning
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