Vulnerability of Regional Aviation Networks Based on DBSCAN and Complex Networks

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.

Community Detection in Aviation Network Based on K-means and Complex Network

With the increasing number of airports and the expansion of their scale,the aviation network has become complex and hierarchical.In order to investigate the complex network characteristics of aviation networks,this paper constructs a Chinese aviation network model and carries out related research based on complex network theory and K-means algorithm.Initially,the P-space model is employed to construct the Chinese aviation network model.Then,complex network indicators such as degree,clustering coefficient,average path length,betweenness and coreness are selected to investigate the complex characteristics and hierarchical features of aviation networks and explore their causes.Secondly,using K-means clustering algorithm,five values are obtained as the initial clustering parameter K values for each of the aviation network hierarchies classified according to five complex network indicators.Meanwhile,clustering simulation experiments are conducted to obtain the visual clustering results of Chinese aviation network nodes under different K values,as well as silhouette coefficients for evaluating the clustering effect of each indicator in order to obtain the hierarchical classification of aviation networks under different indicators.Finally,the silhouette coefficient is optimal when the K value is 4.Thus,the clustering results of the four layers of the aviation network can be obtained.According to the experimental results,the complex network association discovery method combined with K-means algorithm has better applicability and simplicity,while the accuracy is improved.