Graph Neural Networks for Structured Data

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning from structured data, particularly graphs and networks. This paper provides a comprehensive overview of the applications of GNNs in various domains, highlighting their ability to capture complex relationships and dependencies in structured data. We discuss the underlying principles of GNNs, their architecture, and the challenges and opportunities they present. Through a review of recent literature, we showcase the diverse applications of GNNs, including social network analysis, drug discovery, recommendation systems, and computer vision. We also discuss the limitations of current GNN models and propose future research directions to enhance their performance and scalability. Overall, this paper aims to provide researchers and practitioners with a thorough understanding of the capabilities and potential of GNNs in handling structured data.