Graph Neural Networks for Analytics: Advanced Analytics for Relationships and Interactions

Abstract

Graph Neural Networks (GNNs) represent a transformative advancement in machine learning, enabling analytics on data with complex relational structures. Unlike traditional machine learning models that assume independent data samples, GNNs operate on graphs, modeling entities as nodes and relationships as edges. This paper provides a comprehensive overview of GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Recurrent Networks (GRNs), emphasizing their role in advanced analytics for social networks, recommendation systems, financial fraud detection, and biological networks. Applications, model training methodologies, and evaluation metrics are discussed. Challenges related to scalability, interpretability, and dynamic graph modeling are examined. Case studies highlight the practical utility of GNNs in analyzing complex interactions and improving decision-making in real-world systems.

KEYWORDS: Graph Neural Networks, GNN, Social Networks, Recommendation Systems, Fraud Detection, Graph Analytics