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A guide for choosing community detection algorithms in social network studies: The Question-Alignment approach
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10 2020
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Source: Am J Prev Med. 59(4):597-605
[PDF-867.37 KB]
Details:
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Alternative Title:Am J Prev Med
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Personal Author:
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Description:Introduction:
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.
Methods:
Six 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.
Results:
Community detection using the Walktrap method best fit the hypothetical case study. The communities derived via Walktrap were quite different from communities derived via the other five methods in both the number of communities and individuals within communities.
Conclusions:
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 if the Question-Alignment approach translates into improved intervention results.
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Source:
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Pubmed ID:32951683
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Pubmed Central ID:PMC7508227
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Funding:
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Volume:59
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Issue:4
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