Identifying influential waypoints in air route networks based on network agglomeration relative entropy
Identifying influential waypoints in air route networks based on network agglomeration relative entropy
复制标题
基于网络集聚相对熵的航线网络影响航路点识别
DOI：
10.1016/j.cjph.2018.11.003
复制
发表时间：
2019-02-01
影响因子：
5
通讯作者：
Lu, Chaoyang
中科院分区：
文献类型：
--
作者：
Ren, Guangjian;Zhu, Jinfu;Lu, Chaoyang
航线网络是重要的交通网络之一，关键航路点（节点）对航线网络的稳定性和鲁棒性有重要影响。基于网络凝聚和相对熵理论，提出了一种网络凝聚相对熵中心度（NAREC）方法来识别ARN中的影响节点。分析了北京、上海和广州区域ARN的基本拓扑特征，并将该方法应用于这三个区域ARN的影响节点识别。最后，通过SIR模型和Kendall τ系数的计算，验证了NAREC方法的有效性。结果表明，该方法是可行和有效的。
Air route network (ARN) is one of the most important transportation networks and the key waypoints (nodes) have significant influence on the stability and robustness of the ARN. In this paper, a network agglomeration relative entropy centrality (NAREC) method to identify influential nodes in ARNs is proposed, based on the network agglomeration and relative entropy theory. The basic topological features of the regional ARNs in Beijing, Shanghai and Guangzhou are analyzed and then the proposed method is applied to identifying influential nodes in the three networks. At last, the effectiveness of the NAREC method is demonstrated by the susceptible-infected-removed (SIR) model and the Kendall's tau coefficient. Results show that the proposed method is applicable and effective.