A survey of graph neural networks and their industrial applications

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and modeling graph-structured data. In recent years, GNNs have gained significant attention in various domains. This review paper aims to provide an overview of the state-of-the-art graph neural network techniques and their industrial applications. First, we introduce the fundamental concepts and architectures of GNNs, highlighting their ability to capture complex relationships and dependencies in graph data. We then delve into the variants and evolution of graphs, including directed graphs, heterogeneous graphs, dynamic graphs, and hypergraphs. Next, we discuss the interpretability of GNN, and GNN theory including graph augmentation, expressivity, and over-smoothing. Finally, we introduce the specific use cases of GNNs in industrial settings, including finance, biology, knowledge graphs, recommendation systems, Internet of Things (IoT), and knowledge distillation. This review paper highlights the immense potential of GNNs in solving real-world problems, while also addressing the challenges and opportunities for further advancement in this field.