Graph neural networks (GNNs) have gained significant attention in recent years due to their ability to model relational data. GNNs provide a robust framework for learning from graph-structured data, which is widespread in many real-world applications, such as social networks, recommendation systems, bioinformatics, and disaster management, and more. Unlike traditional neural networks, which are designed for Euclidean data (e.g., images or sequences), GNNs are specifically designed to handle data where the relationships between data points are crucial.