Graph Neural Networks - Models and Applications: Studying graph neural network models and applications for learning from graph-structured data such as social networks and molecules

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful class of models for learning from graph-structured data. In recent years, GNNs have shown remarkable success in various applications, including social network analysis, bioinformatics, recommendation systems, and knowledge graphs. This paper provides a comprehensive overview of GNN models and their applications. We begin by discussing the fundamentals of graph representation learning and the architecture of GNNs. We then delve into different types of GNNs, such as Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE. We also highlight key advancements in GNNs, including scalability, interpretability, and robustness. Furthermore, we survey the applications of GNNs across different domains, including social network analysis, molecular chemistry, and recommendation systems. Finally, we discuss challenges and future directions in the field of GNNs.