To enhance the accuracy of performance analysis of regional airline network,this study applies complex network theory and Density-Based Spatial Clustering of Applications with Noise(DBSCAN)algorithm to investigate the...To enhance the accuracy of performance analysis of regional airline network,this study applies complex network theory and Density-Based Spatial Clustering of Applications with Noise(DBSCAN)algorithm to investigate the topology of regional airline network,constructs node importance index system,and clusters 161 airport nodes of regional airline network.Besides,entropy power method and approximating ideal solution method(TOPSIS)is applied to comprehensively evaluate the importance of airport nodes and complete the classification of nodes and identification of key points;adopt network efficiency,maximum connectivity subgraph and network connectivity as vulnerability measurement indexes,and observe the changes of vulnerability indexes of key nodes under deliberate attacks and 137 nodes under random attacks.The results demonstrate that the decreasing trend of the maximum connectivity subgraph indicator is slower and the decreasing trend of the network efficiency and connectivity indicators is faster when the critical nodes of the regional airline network are deliberately attacked.Besides,the decreasing trend of the network efficiency indicator is faster and the decreasing trend of the maximum connectivity subgraph indicator is slower when the nodes of four different categories are randomly attacked.Finally,it is proposed to identify and focus on protecting critical nodes in order to better improve the security level of regional airline system.展开更多
文摘To enhance the accuracy of performance analysis of regional airline network,this study applies complex network theory and Density-Based Spatial Clustering of Applications with Noise(DBSCAN)algorithm to investigate the topology of regional airline network,constructs node importance index system,and clusters 161 airport nodes of regional airline network.Besides,entropy power method and approximating ideal solution method(TOPSIS)is applied to comprehensively evaluate the importance of airport nodes and complete the classification of nodes and identification of key points;adopt network efficiency,maximum connectivity subgraph and network connectivity as vulnerability measurement indexes,and observe the changes of vulnerability indexes of key nodes under deliberate attacks and 137 nodes under random attacks.The results demonstrate that the decreasing trend of the maximum connectivity subgraph indicator is slower and the decreasing trend of the network efficiency and connectivity indicators is faster when the critical nodes of the regional airline network are deliberately attacked.Besides,the decreasing trend of the network efficiency indicator is faster and the decreasing trend of the maximum connectivity subgraph indicator is slower when the nodes of four different categories are randomly attacked.Finally,it is proposed to identify and focus on protecting critical nodes in order to better improve the security level of regional airline system.
