When performing social network analytics, regardless of discipline, it is important to understand the question you are asking before you start combing for data. With data analytics tools like NumPy and NetworkX, you may find yourself computing four centrality measures because it’s easy and available, but end up with twice as many values to scratch your head over. The difference between some centrality measures may seem subtle, but knowing which to use for your research question could save you from a lot of headaches at the hands of your colleagues or IRB.
Degree centrality, for example, is the easiest to understand and compute of any centrality measure; for that reason it may seem appealing for your research. Entire social network analytics tools are built on the presumption that more connections = better, or more prestigious, or more influential. This may not always be the case in regards to identifying internet network structures. Degree centrality may be helpful for identifying the Sean King’s in your network, however it could also be responsible for finding the SpamBot78887’s and Lil B’s.
Consider this forthcoming article by Hyat et. al which investigates gendered discourse patterns in online social networks. By scraping demographic information, friendship networks, and forum activity from 21,000 of MyMarker Cafe‘s members, the authors sought to investigate the role of gender in shaping the structure of online discussion boards. The authors determined that men had a higher mean degree centrality value than women, and were more likely to start threads than women. However, women were more likely to make connections through their comments on other threads, and their posts were often held in higher regard by other users than those of their male counterparts.
The authors concluded that men still dominated discourse on MyMarker Cafe. However, if their posts weren’t as popular, and women were more connected to active participants, does degree centrality tell the whole story? Do women connect to more prominent users, making Eigenvector centrality a more appropriate measure? Does their level of activity put them at the heart of the network, making closeness centrality a better measure? Degree centrality might give a pithier answer to their research question, but in this case might not be showing the whole picture.
A better example of centrality-done-right can be seen in a 2016 article investigating floodplain management efforts in the Netherlands. Using social network analysis, Fliervoet et. al set out to investigate collaborative efforts between government organizations and local NGO’s. The data encapsulates two networks: flood protection groups and nature preservation groups, both of whom’s interests intersect at the floodplains. The authors calculated both degree centrality and betweenness centrality measures in their data analysis. As the authors hypothesized, degree centrality values were higher among government organizations than NGO’s. However, betweenness centrality tells a much more interesting story. Certain governmental organizations serve as liaisons between both networks, which were otherwise loosely connected. These organizations displayed similar degree centrality values to other governmental organizations, but in the context of the research played a much more important role: they encouraged collaboration between public groups.
Social Network Analysis techniques are valuable because they allow us to investigate dynamic relationships within a network. As researchers, we need to make sure our methods are just as dynamic in order for our analyses to be as accurate as possible.