Two betweenness centrality measures based on Randomized Shortest Paths.
Two betweenness centrality measures based on Randomized Shortest Paths.
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DOI：
10.1038/srep19668
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发表时间：
2016-02-01
影响因子：
4.6
通讯作者：
Saerens M
中科院分区：
文献类型：
--
作者：
Kivimäki I;Lebichot B;Saramäki J;Saerens M
本文介绍了两个新的密切相关的介数中心性措施的基础上随机最短路径（RSP）的框架，填补了传统的网络中心性措施之间的差距，最短路径和最近的方法考虑随机游走或电流。该框架定义了网络路径上的玻尔兹曼概率分布，其重点是最短路径，但也考虑了取决于逆温度参数的较长路径。RSP以前已被证明在定义网络上的距离测量方面是有用的。在这项工作中，我们研究他们的效用在量化的网络节点的重要性。建议的RSP介数中心联合收割机，以最佳的方式，使用最短和纯随机路径的想法，分析网络节点的角色，避免涉及这两个范例的问题。我们提出了这些措施的衍生物，以及它们如何可以计算在一个有效的方式。此外，我们显示与真实的世界的例子的潜在的RSP介数中心在确定感兴趣的节点的网络，更传统的方法可能无法注意到。
This paper introduces two new closely related betweenness centrality measures based on the Randomized Shortest Paths (RSP) framework, which fill a gap between traditional network centrality measures based on shortest paths and more recent methods considering random walks or current flows. The framework defines Boltzmann probability distributions over paths of the network which focus on the shortest paths, but also take into account longer paths depending on an inverse temperature parameter. RSP’s have previously proven to be useful in defining distance measures on networks. In this work we study their utility in quantifying the importance of the nodes of a network. The proposed RSP betweenness centralities combine, in an optimal way, the ideas of using the shortest and purely random paths for analysing the roles of network nodes, avoiding issues involving these two paradigms. We present the derivations of these measures and how they can be computed in an efficient way. In addition, we show with real world examples the potential of the RSP betweenness centralities in identifying interesting nodes of a network that more traditional methods might fail to notice.