Abstract
This chapter explores the diverse applications of graph neural networks (GNNs), a powerful class of neural networks designed to work directly with graph-structured data. We begin with an overview of the fundamental principles of GNNs, highlighting their ability to capture difficult relationships and dependencies in data represented as graphs. The chapter is organized into several key application domains, including social network analysis, where GNNs are used to predict user behaviour and enhance recommendation systems; bioinformatics, where they facilitate drug discovery and protein-protein interaction prediction; and natural language processing, where they assist in semantic understanding and relation extraction. Additionally, we delve into the realm of computer vision, demonstrating how GNNs can improve object detection and scene understanding by modelling spatial relationships. Furthermore, we examine emerging applications in areas such as financial fraud detection, traffic prediction, and knowledge graph completion. Each section discusses specific case studies, the architecture of GNNs employed, and the results achieved, underscoring the versatility and effectiveness of GNNs across various fields. In conclusion, we reflect on the future of GNN research, highlighting potential challenges and opportunities for innovation, including the need for scalability, interpretability, and integration with other machine learning frameworks. This chapter serves as a comprehensive resource for researchers and practitioners looking to harness the power of GNNs in their respective domains.
