Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Am J Prev Med. 2020 Oct;59(4):597-605. doi: 10.1016/j.amepre.2020.04.015.
Community detection, the process of identifying subgroups of highly connected individuals within a network, is an aspect of social network analysis that is relevant but potentially underutilized in prevention research. Guidance on using community detection methods stresses aligning methods with specific research questions but lacks clear operationalization. The Question Alignment approach was developed to help address this gap and promote the high-quality use of community detection methods.
A total of 6 community detection methods are discussed: Walktrap, Edge-Betweenness, Infomap, Louvain, Label Propagation, and Spinglass. The Question Alignment approach is described and demonstrated using real-world data collected in 2013. This hypothetical case study was conducted in 2019 and focused on targeting a hand hygiene intervention to high-risk communities to prevent influenza transmission.
Community detection using the Walktrap method best fit the hypothetical case study. The communities derived using the Walktrap method were quite different from communities derived through the other 5 methods in both the number of communities and individuals within communities. There was evidence to support that the Question Alignment approach can help researchers produce more useful community detection results. Compared to other methods of selecting high-risk groups, the Walktrap produced the most communities that met the hypothetical intervention requirements.
As prevention research incorporating social networks increases, researchers can use the Question Alignment approach to produce more theoretically meaningful results and potentially more useful results for practice. Future research should focus on assessing whether the Question Alignment approach translates into improved intervention results.
社区检测是识别网络中高度连接个体亚组的过程，是社交网络分析的一个方面，在预防研究中具有相关性，但可能未被充分利用。关于使用社区检测方法的指南强调了将方法与特定研究问题保持一致，但缺乏明确的操作化。问题对齐方法是为了解决这一差距并促进社区检测方法的高质量使用而开发的。
共讨论了 6 种社区检测方法：Walktrap、Edge-Betweenness、Infomap、Louvain、Label Propagation 和 Spinglass。描述并展示了使用 2013 年收集的真实数据的问题对齐方法。这个假设性案例研究于 2019 年进行，重点是针对高风险社区进行手卫生干预以预防流感传播。
Walktrap 方法的社区检测最适合假设案例研究。通过 Walktrap 方法得出的社区在社区数量和社区内个体方面与通过其他 5 种方法得出的社区有很大的不同。有证据支持问题对齐方法可以帮助研究人员产生更有用的社区检测结果。与其他选择高风险群体的方法相比，Walktrap 产生的符合假设干预要求的社区最多。
随着包含社交网络的预防研究的增加，研究人员可以使用问题对齐方法来产生更具理论意义的结果，并且可能对实践更有用的结果。未来的研究应侧重于评估问题对齐方法是否转化为干预结果的改善。
