Analysis of the container shipping network (CSN) a maximum flow disruption perspective

The global Container Shipping Network (CSN) is a vital infrastructure supporting international trade. Its resilience is crucial for maintaining the stability of global supply chains. This study constructs a weighted directed graph model from the perspective of port-level disruptions to systematically assess the contribution and vulnerability of individual ports to regional and global accessibility. Incorporating regional segmentation and a super-node mechanism, the CSN is constructed using AIS data. The network includes 1,304 nodes and 19,721 edges, divided into ten geographical regions. Three disruption scenarios—random, targeted, and regional—are designed to simulate functional failures in port exports, imports, and bidirectional operations. Network degradation is quantified using maximum flow as the primary performance metric. Results indicate that ports such as Shanghai, Singapore, Tangier, and Jebel Ali play critical roles in maintaining both global and regional connectivity. Compared to random disruptions, targeted attacks reduce network connectivity about 200% more effectively, while regional disruptions cause a sharper decline in flow during the early stages. Further analysis shows that for key ports, unidirectional disruptions—such as banning only exports or only imports—can reduce maximum flow by 80% to 90% compared to full bidirectional shutdowns. This suggests a high degree of functional symmetry between export and import flows. Even single-direction failures can cause substantial losses in logistics accessibility, far exceeding the conventional expectation that partial disruptions would produce proportionally smaller impacts. These findings offer theoretical insights for optimizing port configurations and evaluating strategies to mitigate geopolitical risks.