By Iris Smith, Ph.D.

Social network analysis (SNA) is a research paradigm used to examine the pattern of relationships within a social structure or system to understand how individual components interact and influence each other.  For example, SNA can be used to examine communication patterns within a community to identify opinion leaders, patterns of knowledge diffusion, linkages between individuals, groups or organizations, and how these linkages influence a desired outcome.  This information can be helpful during prevention planning, implementation, and evaluation. 

There are two types of analytic methods used in network analysis: visualization and mathematical analysis.  Visualization analysis involves the construction of a graphical summary of interactions within a network.  For example, in the visualization below (Figure 1), each circle represents an individual within a social network.  The lines connecting the circles (called vertices) represent interactions or communication between individuals in the network.  In this visualization, the individual (or organization) represented by the orange circle is central to the communication within this network.  In social network analysis this is defined as “betweenness” and illustrates how individuals in this network interact and communicate with one another.  It also shows that while the group of blue individuals on the left appear to be interconnected, the two blue individuals on the right and the yellow individual on the left seem to be isolated and are only connected to the network through a single individual.  You might conclude that the individual represented by the orange circle would be an important person to influence since she or he is connected to all the other individuals in the network and may even be considered an opinion leader.

Figure 1

The length, thickness, or directionality of the lines can also be used to represent proximity, frequency of communication, or other characteristics of the network. Network graphs can be generated using several statistical programs such as R, SPSS, and SAS. While mathematical computer-generated models can be quite complex, simple visualizations can also be created manually.

A  2019 scoping review identified 27 published studies that used SNA to examine networks involving health professionals to determine how knowledge was transferred and how the knowledge transfer influenced outcomes related to health behaviors, attitudes, or innovations  The studies included in the review used SNA in a variety of ways including examinations of patterns and efficiency of information sharing among health professionals, identifying positions of influence, similarity of attributes among system components, and determining the effectiveness of interventions.¹ SNA has been used extensively to study adolescent health behaviors including substance use.  A 2022 systematic review identified 201 studies that used SNA to study adolescent health behaviors such as peer influence, substance use, and the spatial context in which social relationships take place.  These researchers developed a 5-step decision tree to guide researchers interested in applying SNA methods (https://ars.els-cdn.com/content/image/1-s2.0-S0277953622008255-gr3_lrg.jpg).²

Resources

Burgette, J. M., Rankine, J., Culyba, A. J., Chu, K. H., & Carley, K. M. (2021). Best Practices for Modeling Egocentric Social Network Data and Health Outcomes. HERD, 14(4), 18–34. https://doi.org/10.1177/19375867211013772

Collonnaz, M., Riglea, T., Kalubi, J., O'Loughlin, J., Naud, A., Kestens, Y., Agrinier, N., & Minary, L. (2022). Social network analysis to study health behaviours in adolescents: A systematic review of methods. Social science & medicine (1982), 315, 115519. https://doi.org/10.1016/j.socscimed.2022.115519

Glegg S,JenkinsE, Kathari A (2019) How the Study of Networks Informs Knowledge Translation and Implementation: A Scoping Review.  Implementation Science 14; pg 34. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437864/#CR54  

Saqr, M., López-Pernas, S., Conde-González, M.Á., Hernández-García, Á. (2024). Social Network Analysis: A Primer, a Guide and a Tutorial in R. In: Saqr, M., López-Pernas, S. (eds) Learning Analytics Method and Tutorials.  Springer, Cham.  https://rdcu.be/dMk1I

¹ Glegg S,JenkinsE, Kathari A (2019)  How the Study of Networks Informs Knowledge Translation and Implementation:  A Scoping Review.  Implementation Science 14; pg 34. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437864/#CR54

² Collonnaz, M., Riglea, T., Kalubi, J., O'Loughlin, J., Naud, A., Kestens, Y., Agrinier, N., & Minary, L. (2022). Social network analysis to study health behaviours in adolescents: A systematic review of methods. Social science & medicine (1982), 315, 115519. https://doi.org/10.1016/j.socscimed.2022.115519

Social network
analysis (SNA) is a research paradigm used to examine the pattern of
relationships within a social structure or system to understand how individual
components interact and influence each other. 
For example, SNA can be used to examine communication patterns within a
community to identify opinion leaders, patterns of knowledge diffusion,
linkages between individuals, groups or organizations, and how these linkages
influence a desired outcome.  This
information can be helpful during prevention planning, implementation, and
evaluation.  There are two types of
analytic methods used in network analysis: visualization and mathematical
analysis.  Visualization analysis
involves the construction of a graphical summary of interactions within a
network.  For example, in the
visualization below (Figure 1), each circle represents an individual within a
social network.  The lines connecting the
circles (called vertices) represent interactions or communication between
individuals in the network.  In this
visualization, the individual (or organization) represented by the orange
circle is central to the communication within this network.  In social network analysis this is defined as
“betweenness” and illustrates how individuals in this network interact and
communicate with one another.  It also
shows that while the group of blue individuals on the left appear to be
interconnected, the two blue individuals on the right and the yellow individual
on the left seem to be isolated and are only connected to the network through a
single individual.  You might conclude
that the individual represented by the orange circle would be an important
person to influence since she or he is connected to all the other individuals
in the network and may even be considered an opinion leader.

Figure 1

The length,
thickness, or directionality of the lines can also be used to represent
proximity, frequency of communication, or other characteristics of the network.
Network graphs can be generated using several statistical programs such as R,
SPSS, and SAS. While mathematical computer-generated models can be quite
complex, simple visualizations can also be created manually.

A  2019 scoping review identified 27 published
studies that used SNA to examine networks involving health professionals to
determine how knowledge was transferred and how the knowledge transfer
influenced outcomes related to health behaviors, attitudes, or innovations  The studies included in the review used SNA
in a variety of ways including examinations of patterns and efficiency of
information sharing among health professionals, identifying positions of
influence, similarity of attributes among system components, and determining
the effectiveness of interventions.^[1]
SNA has been used extensively to study adolescent health behaviors including
substance use.  A 2022 systematic review
identified 201 studies that used SNA to study adolescent health behaviors such
as peer influence, substance use, and the spatial context in which social
relationships take place.  These
researchers developed a 5-step decision tree to guide researchers interested in
applying SNA methods (https://ars.els-cdn.com/content/image/1-s2.0-S0277953622008255-gr3_lrg.jpg).^[2]

Resources

Burgette,
J. M., Rankine, J., Culyba, A. J., Chu, K. H., & Carley, K. M. (2021). Best
Practices for Modeling Egocentric Social Network Data and Health
Outcomes. HERD, 14(4), 18–34.
https://doi.org/10.1177/19375867211013772

Collonnaz, M., Riglea, T., Kalubi, J., O'Loughlin, J.,
Naud, A., Kestens, Y., Agrinier, N., & Minary, L. (2022). Social
network analysis to study health behaviours in adolescents: A systematic review
of methods. Social science & medicine (1982), 315,
115519. https://doi.org/10.1016/j.socscimed.2022.115519

Glegg S,JenkinsE, Kathari A (2019) How
the Study of Networks Informs Knowledge Translation and Implementation: A
Scoping Review.  Implementation
Science 14; pg 34. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6437864/#CR54  

Saqr, M., López-Pernas, S., Conde-González, M.Á.,
Hernández-García, Á. (2024). Social Network Analysis: A Primer, a Guide and
a Tutorial in R. In: Saqr, M., López-Pernas, S. (eds) Learning Analytics Method
and Tutorials.  Springer, Cham.  https://rdcu.be/dMk1I