Resistance distance has been studied extensively in the past years, with the majority of previous studies devoted to undirected networks, in spite of the fact that various realistic networks are directed. Although several generalizations of resistance distance on directed graphs have been proposed, they either have no physical interpretation or are not a metric. In this paper, we first extend the definition of resistance distance to strongly connected directed graphs based on random walks and show that the two-node resistance distance on directed graphs is a metric. Then, we introduce the Laplacian matrix for directed graphs that subsumes the Laplacian matrix of undirected graphs as a particular case and use its pseudoinverse to express the two-node resistance distance, and many other relevant quantities derived from resistance distances. Moreover, we define the resistance distance between a vertex and a vertex group on directed graphs and further define a problem of optimally selecting a group of fixed number of nodes, such that their resistance distance is minimized. Since this combinatorial optimization problem is NP-hard, we present a greedy algorithm with a proved approximation ratio, and conduct experiments on model and realistic networks to validate the performance of this approximation algorithm.
翻译:过去几年来,人们广泛研究了抗药性距离,尽管各种现实的网络是针对非定向网络的,但以往的大部分研究都专门针对非定向网络。虽然在定向图上提出了几种抗药性距离的概括,但它们要么没有物理解释,要么不是量度。在本文中,我们首先将抗药性距离的定义扩展至基于随机行走的紧密相连的定向图表,并表明定向图上的双节抗药性距离是一种量度。然后,我们引入了拉普拉西亚矩阵,用于将非定向图的拉普拉西亚矩阵作为特定案例,并使用其假冒反射来表达双点抗药性距离,以及从抗药性距离得出的许多其他相关数量。此外,我们界定了在定向图上脊椎和脊椎组之间的抗药性距离,并进一步界定了最佳选择一组固定节点的抗药性距离问题,从而将它们的抗药性距离降到最低程度。由于这种组合式精度优化问题非常严重,我们提出了一种贪婪的算法,并进行了模型和现实的网络实验,以验证这种精确的算法。