Algorithms and Applications of Graph Neural Network
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 32
Special Issue Editor
Special Issue Information
Dear Colleagues,
Algorithms and Applications of Graph Neural Networks (GNNs) are increasingly vital for learning over graph-structured data, offering a principled framework to capture dependencies in graphs that arise in diverse domains such as social networks, molecular biology, recommendation systems, and power and energy systems. Advances in GNN algorithms not only deepen our understanding of graph-based representations but also enhance scalability, expressiveness, and generalization across tasks involving complex relational structures. The development of efficient and theoretically grounded GNN architectures facilitates interdisciplinary research, opening new frontiers in both foundational theory and applications.
Recent studies also demonstrate powerful synergies between Graph Neural Networks (GNNs) and Large Language Models (LLMs), where graph representations can enhance LLM reasoning, and LLMs can, in turn, guide GNN-based learning through natural language supervision, instruction tuning, or symbolic prompting. This interplay creates exciting opportunities for foundation models that integrate topology-aware learning with rich semantic understanding, paving the way for new advances in multi-modal reasoning, explainability, and knowledge-intensive applications. Furthermore, robust and interpretable GNN methods are crucial for real-world deployment, where transparency and reliability are paramount. Thus, progress in GNN algorithm design and deployment directly contributes to bridging the gap between theoretical advances and high-impact applications across science, engineering, and society.
Dr. Yuzhou Chen
Guest Editor
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Keywords
- graph machine learning
- graph deep learning
- graph neural networks
- robustness analysis
- explainability and interpretability
- transfer learning
- large language models
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