Complex network-based analysis of critical defects in flag state control

In an effort to identify critical defects in ships subject to flag state control (FSC) and mitigate the risk of detention, this study undertakes an examination of ship defects observed during FSC inspections. Drawing upon a sample of 500 FSC inspection cases conducted at select Chinese ports between 2020 and 2022, we utilize Python tools to extract and compile the defective factors, subsequently employing the Apriori algorithm to uncover association rules among these factors. Subsequently, we develop a network model to represent the landscape of FSC ship defects, wherein nodes represent specific defects and edges denote the interrelationships among them. Leveraging this model, we conduct a comprehensive analysis of the network's structure using complex network indices, including betweenness centrality, clustering coefficient, and degree, with the aim of identifying critical ship defects.