Introduction to Graph Machine Learning

The article introduces the principles and methodologies of Graph Machine Learning (GML), emphasizing the use of graph neural networks (GNNs) for tasks such as node classification, link prediction, and graph classification. It discusses key architectures like Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), highlighting their ability to capture relational data structures. Understanding GML is crucial for practitioners as it enables the development of models that leverage complex relationships in data, enhancing performance in domains like social network analysis and recommendation systems.