基于度和聚类系数的中国航空网络重要性节点分析

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Title:

Analysis of key nodes in China’s aviation network basedon the degree centrality indicator and clustering coefficient

作者:

闫玲玲^1,2, 陈增强^1,2,3, 张青³

1. 南开大学 计算机与控制工程学院, 天津 300350;
2. 南开大学 智能机器人技术天津市重点实验室, 天津 300350;
3. 中国民航大学 理学院, 天津 300300

Author(s):

YAN Lingling^1,2, CHEN Zengqiang^1,2,3, ZHANG Qing³

1. College of Computer and Control Engineering, Nankai University, Tianjin 300350, China;
2. Key Laboratory of Intelligent Robotics of Tianjin, Nankai University, Tianjin 300350, China;
3. College of Science, Civil Aviation University of China, Tianjin 300300, China

关键词:

航空网络; 节点重要性; 度; 聚类系数; 复杂网络

Keywords:

aviation network; key nodes; degree; clustering coefficient; complex network

分类号:

N94

DOI:

10.11992/tis.201601024

摘要:

运用度中心性、接近中心性、介数中心性、特征向量中心性和半局部中心性5种方法，对中国航空网络进行节点重要性排序；对重要节点分别进行蓄意攻击和随机攻击，采用脆弱性指标验证排序方法的有效性，仿真结果表明介数中心性能够更准确地刻画中国航空网络中节点的重要性；在航空网络的背景下，将节点的直接影响力和节点邻居之间连接的紧密程度结合起来，提出了一种基于度和聚类系数的新指标，经中国航空网络实例验证，该指标的评价准确性仅次于介数中心性，但是其时间复杂度比介数中心性低很多。

Abstract:

This paper determines the key nodes of China’s aviation network based on degree centrality, closeness centrality,‘betweenness’centrality, eigenvector centrality, semi-local centrality indicators, and then ranks these nodes in descending order of importance. Using a vulnerability index and reviewing risks from deliberate and random attack the effectiveness of the sorting methods is then evaluated. It is apparent from the corresponding vulnerability indices that the aviation network of China is most vulnerable to targeted attacks according to the betweenness centrality indicator. Moreover, based on the aviation network, this paper proposes a new evaluation method, which takes into account not only the number of neighbors, but also the clustering coefficient. Focusing on China’s aviation network, the experimental results demonstrate that the evaluation accuracy of the new index ranks only second to the betweenness centrality, and is more efficient compared with betweenness centrality as regards time complexity.