在COVID-19大流行期间使用社交网络分析方法识别大学宿舍中的凝聚子群
Identification of cohesive subgroups in a university hall of residence during the COVID-19 pandemic using a social network analysis approach
AI总结：
本研究采用社交网络分析与Girvan-Newman等算法对43名同宿舍大学生进行案例研究，发现网络最佳划分为4个高内聚子群，且子群成员的性别、种族及居住建筑等属性与COVID-19感染聚类程度显著相关。
摘要：
研究目的包括：(i) 分析大学生子群之间的连通性；(ii) 评估哪些关系联系的桥梁对于连接或断开子群是必不可少的；(iii) 探究子群节点属性的相似性与大流行背景的关联。在COVID-19大流行期间，年轻的大学生经历了人际关系的重大变化，尤其是在宿舍中。以往研究表明了关系结构在传染过程中的重要性。然而，在学生密切共同生活的大学环境中，仍缺乏相关研究。本研究采用案例研究方法进行了一项描述性研究。参与者由居住在同一宿舍的43名大学生组成。数据分析采用了社交网络分析（SNA）。应用了Factions和Girvan Newman算法来检测现有的凝聚子群。UCINET工具用于计算SNA指标。使用Gephi软件对全局网络进行了可视化。在应用Girvan-Newman和Factions算法后，两种情况均发现，将网络划分为4个子群是最佳的分组方式。子群内部具有高凝聚力，而子群之间凝聚力较低。子群成员身份与性别之间的关系是显著的。COVID-19感染程度与学生之间的聚类程度相关。大学生在宿舍中形成子群。社交网络分析有助于理解大流行期间的结构行为。该研究提供了证据，证明性别、种族及其居住的建筑在创造网络结构方面的重要性，这些结构在大流行期间可能促进或阻碍传染。
英文摘要：
The aims: (i) analyze connectivity between subgroups of university students, (ii) assess which bridges of relational contacts are essential for connecting or disconnecting subgroups and (iii) to explore the similarities between the attributes of the subgroup nodes in relation to the pandemic context. During the COVID-19 pandemic, young university students have experienced significant changes in their relationships, especially in the halls of residence. Previous research has shown the importance of relationship structure in contagion processes. However, there is a lack of studies in the university setting, where students live closely together. The case study methodology was applied to carry out a descriptive study. The participation consisted of 43 university students living in the same hall of residence. Social network analysis has been applied for data analysis. Factions and Girvan Newman algorithms have been applied to detect the existing cohesive subgroups. The UCINET tool was used for the calculation of the SNA measure. A visualization of the global network will be carried out using Gephi software. After applying the Girvan-Newman and Factions, in both cases it was found that the best division into subgroups was the one that divided the network into 4 subgroups. There is high degree of cohesion within the subgroups and a low cohesion between them. The relationship between subgroup membership and gender was significant. The degree of COVID-19 infection is related to the degree of clustering between the students. College students form subgroups in their residence. Social network analysis facilitates an understanding of structural behavior during the pandemic. The study provides evidence on the importance of gender, race and the building where they live in creating network structures that favor, or not, contagion during a pandemic.
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