基于社区结构与多层次赋权的城市轨道交通网络关键节点识别OA

This study investigates critical node identification in urban rail transit networks to address the limitations of existing methods,which often overlook community structure and differences in macro-level importance,thereby failing to comprehensively characterize the roles of nodes in both overall network organization and local connectivity.First,a critical node identification model integrat-ing community structure and multi-scale node centrality is constructed.An improved Louvain algo-rithm based on consensus clustering is adopted to partition the rail transit network into communities,which are then abstracted as super-nodes.Subsequently,a weighted PageRank algorithm is used to quantify the macro-level importance of these communities.Second,at the node level,two micro-level centrality indicators,namely node strength and weighted betweenness,are selected.Grey relational analysis is applied to determine the indicator weights,and the macro-level community importance is in-troduced to revise the initial node importance,thereby establishing a comprehensive node importance evaluation model.Finally,an empirical analysis is conducted using the Beijing rail transit network as a case study.Deliberate attack simulations are performed to verify the model's identification efficacy and the rationality of the node rankings.The results indicate that the Beijing rail transit network exhibits significant community structure characteristics,with varying macro-level importance across different communities.The identified critical nodes are primarily concentrated at hubs featuring multi-line inter-sections and cross-regional connections,displaying distinct spatial agglomeration.These nodes play a vital role in maintaining network connectivity,cross-regional links,and overall network stability.Un-der deliberate attacks simulated according to the model's identification results,network performance degrades more rapidly.This demonstrates that the proposed model can effectively identify nodes with a critical impact on network stability,providing a novel analytical perspective and methodological refer-ence for critical node identification,resilience enhancement,and the operational management of urban rail transit networks.