Centrality-based identification of important edges in complex networks
Centrality-based identification of important edges in complex networks
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DOI：
10.1063/1.5081098
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发表时间：
2019-03-01
期刊：
影响因子：
2.9
通讯作者：
Lehnertz, Klaus
中科院分区：
文献类型：
--
作者：
Broehl, Timo;Lehnertz, Klaus
中心性是网络科学中最基本的度量标准之一。尽管有大量的方法来测量单个顶点的中心性，但到目前为止，只有少数度量单个边的中心性。我们修改各种，广泛使用的顶点的中心性的概念，为边缘，为了找到在网络中的边缘是重要的其他对顶点之间。侧重于边缘的重要性，我们提出了一个基于边缘中心的网络分解技术，以确定一个层次结构的边缘集，其中每一组都与不同的重要性级别。我们使用各种范式网络模型评估我们的方法的效率，并应用新的概念来识别重要的边缘和重要的边缘集在社会网络分析中常用的基准模型，以及在不断发展的癫痫脑网络。由AIP Publishing授权出版。
Centrality is one of the most fundamental metrics in network science. Despite an abundance of methods for measuring centrality of individual vertices, there are by now only a few metrics to measure centrality of individual edges. We modify various, widely used centrality concepts for vertices to those for edges, in order to find which edges in a network are important between other pairs of vertices. Focusing on the importance of edges, we propose an edge-centrality-based network decomposition technique to identify a hierarchy of sets of edges, where each set is associated with a different level of importance. We evaluate the efficiency of our methods using various paradigmatic network models and apply the novel concepts to identify important edges and important sets of edges in a commonly used benchmark model in social network analysis, as well as in evolving epileptic brain networks. Published under license by AIP Publishing.