Helander Mary E, McAllister Sarah
1IBM T. J. Watson Research Center, Applied Data Science, P.O. Box 218, Yorktown Heights, 10598 NY USA.
2CUNY Borough of Manhattan Community College, Department of Mathematics, 199 Chambers Street, New York, 10007 NY USA.
Appl Netw Sci. 2018;3(1):7. doi: 10.1007/s41109-018-0063-6. Epub 2018 May 10.
We describe a methodology for characterizing the relative structural importance of an arbitrary network edge by exploiting the properties of a -shortest path algorithm. We introduce the metric , measuring how often an edge occurs in any possible network path, as well as -Gravity, a lower bound based on paths enumerated while solving the -shortest path problem. The methodology is demonstrated using Granovetter's original network examples as well as the well-known Florentine families of the Italian Renaissance and the Krebs 2001 terrorist networks. The relationship to edge betweenness is established. It is shown that important edges, i.e. ones with a high , are not necessarily adjacent to nodes of importance as identified by standard centrality metrics, and that key nodes, i.e. ones with high centrality, often have their importance bolstered by being adjacent to -e.g. ones with low . It is also demonstrated that distinguishes critically important bridges or local bridges from those of lesser structural importance.
我们描述了一种方法,通过利用α-最短路径算法的特性来表征任意网络边的相对结构重要性。我们引入了度量指标β,用于衡量一条边在任何可能的网络路径中出现的频率,以及β-引力,它是基于在解决α-最短路径问题时枚举的路径得出的下限。该方法通过使用格兰诺维特最初的网络示例以及意大利文艺复兴时期著名的佛罗伦萨家族和克雷布斯2001年的恐怖网络进行了演示。建立了与边介数的关系。结果表明,重要的边,即具有高β值的边,不一定与通过标准中心性度量确定的重要节点相邻,而且关键节点,即具有高中心性的节点,其重要性通常会因与低β值的边相邻而得到增强。还证明了β能够区分具有至关重要结构的桥接边或局部桥接边与那些结构重要性较低的边。