Integrating Graph Neural Networks in Machine Learning: Applications in Social Network Analysis and Beyond

Abstract

The graph-structured data found in many real-world applications, including recommendation systems, social networks, and biological networks, can be effectively processed and learned with the help of Graph Neural Networks (GNNs). their capacity to capture dependencies and interactions between data points using graph-based representations, and how GNNs are integrated into machine learning workflows. In this article, we take a look at some of the most important GNN designs and compare and contrast them, covering topics such as GATs, Graph Convolutional Networks (GCNs), and GraphSAGE. Our main focus is on social network analysis, where we show how GNNs may be used for tasks like community recognition, connection prediction, and node classification. We also investigate GNNs' potential uses outside social networks, including as in the fields of medicine, financial crime detection, and information graphs. When tested with relational data, GNNs proved to be far more effective than conventional machine learning models. Our research shows that GNNs are a powerful tool for increasing accuracy in many different types of machine learning problems with complicated, linked datasets.