Using node centrality to understand network dynamics

One of the most helpful aspects of social network analysis, to me, is its use of visuals.  Being able to literally see a picture of a network helps me understand patterns and relationships in a way that neither numerical or textual descriptions can.  Eyeballing the graph is helpful, but social network metrics give us an even deeper picture.  Node centrality measures (betweenness, degree, closeness, and eigenvector) describe specific characteristics of the relationship between nodes.  This infographic briefly describes what these measures can tell us about the network.

(c) Beehaus

Social network analysis is very flexible and can be used to study many different types of networks.  SNA is a promising, albeit underused, methodology in understanding bystander intervention.  Research has shown that norms supporting prosocial bystander behavior increase people’s intent to help, while norms against intervening decrease intent to help (Hoxmeier, Flay, & Ackok, 2016).  Researchers have not, however, used SNA to look at how the group norms are created and spread in the first place.  People who are high in closeness centrality spread new information quickly and efficiently; they could be identified and targeted for specific training.  Identifying people high in betweenness centrality would help prevention educators strategically interject prosocial norms to more isolated parts of the network.  As opposed to training as many people as possible, SNA could help educators efficiently target programming where it would do the most good.

Researchers have used SNA to study gender-based violence response networks.  For example, Rana and Allen (2015) compared the networks of organizations in five separate family violence councils.  They found that the prominent organizations were different in each council.  For example, the domestic violence program was clearly the most important organization in councils A and C as it was highest in all three centralities.  In council D, the prominence of the domestic violence program depended on the role being investigated.  It was highest in betweenness, meaning it was the key broker/bridge in the network.  Child and family services was highest in degree and closeness, meaning it was connected to more organizations and involved in more relationships between organizations.  The researchers discussed the findings in relation to leveraging relationships between organizations to further council goals and initiatives.  The SNA approach allowed for more nuance in understanding the relationships than other analyses would have.

Quinlan and Quinlan (2010) used SNA in a much different way.  Instead of looking at people or organizations, they analyzed institutional and lived experiences of rape.  In the institutional network, the nodes represented the components of the forensic medical exam.  They included physical evidence, survivor history, assault details, and medical professionals.  The links between nodes represented the connections between aspects of the exam.  The lived experience network, however, set the survivor’s feelings, actions, and thoughts as the nodes in the network.  Time (order of what happened when) was represented by the links.

The researchers used node centrality measures to determine the most prominent components of each network.  For the lived experience network, the survivor’s feelings of fear and horror had the highest degree centrality.  This suggested that for this survivor, those feelings were the most powerful aspects of her experience.  For the institutional network, the survivor’s identity had the highest degree centrality and the assault had second highest.  This suggested that the survivor’s identity was more central than any other aspect of the exam process.  In the discussion, the researchers related these findings to the ways that rape is viewed and treated in society.  The use of SNA gave a different and compelling perspective on rape and sexual violence research.

 

References:

Hoxmeier, J. S., Flay, B. R., & Ackok, A. C. (2016). Control, norms, and attitudes: Differences between students who do and do not intervene as bystanders to sexual assault. Journal of Interpersonal Violence, 1-23, doi: 10.1177/0886260515625503

Quinlan, E. & Quinlan, A. (2010). Representations of Rape: Transcending Methodological Divides. Journal of Mixed Methods Research, 4, 127-143.

Rana, S. & Allen, N. E. (2015). Centrality measures to identify key stakeholders in Family Violence Councils. Psychosocial Intervention, 24, 167-176.

One thought on “Using node centrality to understand network dynamics

  • October 4, 2017 at 12:31 pm
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    I love the use of SNA to map the chronology of feelings. I wonder if you can replicate this using discussion boards. Is there an emotional chronology on discussion boards about rape? Or bystander intervention? I wonder if there is an emotional chronology among men who do step in vs. those who don’t?

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