Centrality describes the importance of nodes in a graph and is modeled by various measures. Its global analogue, called centralization, is a general formula for calculating a graph-level centrality score based on the node-level centrality measure. The latter enables us to compare graphs based on the extent to which the connections of a given network are concentrated on a single vertex or group of vertices. One of the measures of centrality in social network analysis is harmonic centrality. It sums the inverse of the geodesic distances of each node to other nodes where it is 0 if there is no path from one node to another, with the sum normalized by dividing it by $m-1$, where $m$ is the number of nodes of the graph. In this paper, we present some results regarding the harmonic centralization of some important families of graphs with the hope that formulas generated herein will be of use when one determines the harmonic centralization of more complex graphs.
翻译:中心度在图形中描述节点的重要性, 并用多种计量模型进行模拟。 其全球类比, 称为集中化, 是计算基于节点中心度测量的图形级中心点评分的一般公式。 后者使我们能够根据特定网络连接集中在单一顶点或一组脊椎上的程度对图表进行比较。 社会网络分析的中心度的衡量标准之一是调和中心度。 它将每个节点与其它节点的大地偏差的逆差进行计算, 如果从一个节点到另一个节点没有一条路径, 其总和通过将之除以m-1美元( $ $) 实现正常化, 后者是图形节点的数量。 在本文中, 我们提出了一些关于某些重要图表组合的调和集中化结果, 希望当确定更复杂的图表的调和集中时, 这里生成的公式将会被使用。