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

Citation

Smith, Natalie R.; Zivich, Paul N.; Frerichs, Leah; Moody, James W.; & Aeillo, Allison E. (2020). A Guide for Choosing Community Detection Algorithms in Social Network Studies: The Question Alignment Approach. American Journal of Preventive Medicine, 59(4), 597-605. PMCID: PMC7508227

Abstract

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.

URL

http://dx.doi.org/10.1016/j.amepre.2020.04.015

Reference Type

Journal Article

Journal Title

American Journal of Preventive Medicine

Author(s)

Smith, Natalie R.
Zivich, Paul N.
Frerichs, Leah
Moody, James W.
Aeillo, Allison E.

Year Published

2020

Volume Number

59

Issue Number

4

Pages

597-605

PMCID

PMC7508227

NIHMSID

NIHMS1588673

Reference ID

12708