Graph Neural Networks: Techniques and Applications for Data Structured as Graphs

Abstract

Graph neural networks (GNNs) have emerged as a powerful tool for machine learning tasks on data structured as graphs, demonstrating sig- nificant advancements across domains such as social network analysis, molecular chemistry, and recommendation systems. This review provides a comprehensive examination of the foundational techniques in GNNs, en- compassing graph convolutional networks, graph attention networks, and spatio-temporal graph neural networks. It explores the algorithms’ ability to leverage graph structures for learning representations that capture the dependencies among the nodes and edges in the data. Additionally, we highlight the broad spectrum of applications in which GNNs are being em- ployed, from graph classification and node classification to link prediction and anomaly detection. The paper also discusses key challenges faced by GNNs, such as scalability, interpretability, and the need for more sophis- ticated models to handle dynamic and heterogeneous graph data. Finally, future directions for research are suggested, focusing on integrating GNNs with other deep learning architectures and enhancing their applicability to diverse scientific domains.