复杂社会网络的中心性测度分析

F. Grando, Diego Noble, L. Lamb
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引用次数: 38

摘要

复杂网络分析的度量,如顶点中心性,有可能揭示现有的网络模式和行为。通过分析网络的结构特性,它们有助于理解网络及其组成部分,这使得它们在几个计算机科学领域和应用中很有用。不幸的是,有大量不同的中心性度量,在实践中对它们的共同特征知之甚少。通过实证分析,我们的目标是清楚地了解可用的主要中心性度量,揭示它们在大量不同的社会网络中的异同。我们的实验表明,被称为信息、特征向量、子图、行走间隔和中间度的顶点中心性度量可以以95%的粒度性能区分各种网络中的顶点,而其他度量则获得了相当低的结果。此外,我们证明了几对度量以非常相似的方式评估顶点,即它们的相关系数值大于0.7。这是出乎意料的,考虑到每个指标都有一个非常不同的理论和算法基础。因此,我们的工作有助于发展有原则的网络分析和评估方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Analysis of Centrality Measures for Complex and Social Networks
Measures of complex network analysis, such as vertex centrality, have the potential to unveil existing network patterns and behaviors. They contribute to the understanding of networks and their components by analyzing their structural properties, which makes them useful in several computer science domains and applications. Unfortunately, there is a large number of distinct centrality measures and little is known about their common characteristics in practice. By means of an empirical analysis, we aim at a clear understanding of the main centrality measures available, unveiling their similarities and differences in a large number of distinct social networks. Our experiments show that the vertex centrality measures known as information, eigenvector, subgraph, walk betweenness and betweenness can distinguish vertices in all kinds of networks with a granularity performance at 95%, while other metrics achieved a considerably lower result. In addition, we demonstrate that several pairs of metrics evaluate the vertices in a very similar way, i.e. their correlation coefficient values are above 0.7. This was unexpected, considering that each metric presents a quite distinct theoretical and algorithmic foundation. Our work thus contributes towards the development of a methodology for principled network analysis and evaluation.
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