Why distinctiveness centrality is distinctive

A. Fronzetti Colladon, M. Naldi
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Abstract

This paper responds to a commentary by Neal (2024) regarding the Distinctiveness centrality metrics introduced by Fronzetti Colladon and Naldi (2020). Distinctiveness centrality offers a novel reinterpretation of degree centrality, particularly emphasizing the significance of direct connections to loosely connected peers within (social) networks. This response paper presents a more comprehensive analysis of the correlation between Distinctiveness and the Beta and Gamma measures. All five distinctiveness measures are considered, as well as a more meaningful range of the {\alpha} parameter and different network topologies, distinguishing between weighted and unweighted networks. Findings indicate significant variability in correlations, supporting the viability of Distinctiveness as alternative or complementary metrics within social network analysis. Moreover, the paper presents computational complexity analysis and simplified R code for practical implementation. Encouraging initial findings suggest potential applications in diverse domains, inviting further exploration and comparative analyses.
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特色中心性为何与众不同
本文回应了 Neal(2024 年)对 Fronzetti Colladon 和 Naldi(2020 年)提出的独特性中心度量的评论。独特性中心度对度中心度进行了新颖的重新诠释,特别强调了(社交)网络中与连接松散的同伴之间直接连接的重要性。这篇回应论文对独特性与 Beta 和 Gamma 测量之间的相关性进行了更全面的分析。本文考虑了所有五种独特性度量,以及更有意义的{\alpha}参数范围和不同的网络拓扑结构,并对加权网络和非加权网络进行了区分。研究结果表明,相关性具有显著的可变性,支持将独特性作为社交网络分析的替代或补充度量的可行性。此外,论文还介绍了计算复杂性分析和用于实际应用的简化 R 代码。令人鼓舞的初步研究结果表明,该方法在不同领域都有潜在的应用前景,需要进一步探索和比较分析。
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