The Core Might Change Anyhow We Define It: The Instability of Key Actors in Longitudinal Social Network Data

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Complexity Pub Date : 2024-03-02 DOI:10.1155/2024/3956877
Róbert Pethes, Eliza Bodor-Eranus, Károly Takács, Levente Kovács
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引用次数: 0

Abstract

Central actors or opinion leaders are in the right structural position to spread relevant information or convince others about adopting an innovation or behaviour change. Who is a central actor or opinion leader might be conceptualised in various ways. Widely accepted centrality measures do not take into account that those in central positions in the social network may change over time. A longitudinal comparison of the set and importance of opinion leaders is problematic with these measures and therefore needs a novel approach. In this study, we investigate ways to compare the stability of the set of central actors over time. Using longitudinal survey data from primary schools (where the members of the social networks do not change much over time) on advice-seeking and friendship networks, we find a relatively poor stability of who is in the central positions anyhow we define centrality. We propose the application of combined indices in order to achieve more efficient targeting results. Our results suggest that because opinion leaders may change over time, researchers should be careful about relying on simple centrality indices from cross-sectional data to gain and interpret information (for example, in the design of prevention programs, network-based interventions or infection control) and must rely on more diverse structural information instead.

无论我们如何定义,核心都可能改变:纵向社会网络数据中关键行为者的不稳定性
中心行为者或舆论领袖处于适当的结构地位,能够传播相关信息或说服他人采用创新或行为改变。谁是核心参与者或意见领袖,可以有多种概念。广为接受的中心度衡量标准并没有考虑到在社会网络中处于中心位置的人可能会随着时间的推移而发生变化。用这些方法对意见领袖的集合和重要性进行纵向比较是有问题的,因此需要一种新的方法。在本研究中,我们探讨了比较中心参与者群体随时间变化的稳定性的方法。利用小学(社会网络成员随时间变化不大)关于寻求建议和友谊网络的纵向调查数据,我们发现,无论我们如何定义中心地位,处于中心位置的人的稳定性都相对较差。我们建议采用综合指数,以获得更有效的目标定位结果。我们的研究结果表明,由于意见领袖可能会随着时间的推移而发生变化,因此研究人员应谨慎依赖横截面数据中的简单中心性指数来获取和解释信息(例如,在设计预防计划、基于网络的干预措施或感染控制时),而必须依赖更多样化的结构信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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