A Guide for Choosing Community Detection Algorithms in Social Network Studies: The Question Alignment Approach

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 spec...

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Veröffentlicht in:American journal of preventive medicine Jg. 59; H. 4; S. 597 - 605
Hauptverfasser: Smith, Natalie R., Zivich, Paul N., Frerichs, Leah M., Moody, James, Aiello, Allison E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier Inc 01.10.2020
Elsevier Science Ltd
Schlagworte:
Algorithms
Alignment
Case studies
Community
Community research
Disease transmission
High risk
Humans
Hygiene
Influenza
Intervention
Operational definitions
Prevention
Prevention programs
Preventive medicine
Public health
Questions
Social network analysis
Social Networking
Social networks
ISSN:0749-3797, 1873-2607, 1873-2607
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Zusammenfassung: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.
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ISSN:0749-3797
1873-2607
1873-2607
DOI:10.1016/j.amepre.2020.04.015