Topology, Visualization & Social Network Analysis

After reading Edwards’ article, Mixed-Method Approaches to Social Network Analysis, I have certainly gained a whole new understanding and awareness of Social Network Analysis (S.N.A.). This field of study was not one that I was familiar with, but after reading the paper, I recognized how similar on certain levels G.I.Science and S.N.A. actually were (which is becoming increasingly common with the more I/we learn in this course).

The first point that I found interesting was how S.N.A. looked at the social relationships between different actors and how and what kinds of things flow within those relationships. An important link between S.N.A. and G.I.Science is the importance of topology. For example, a researcher could create a visual network map (a.k.a. sociogram) of an agent which shows social connections. An important distinction to take into consideration, just like when using a G.I.S., is whether or not the spatial relationship of the connections play an important role. As was noted in the paper, “the nodes at the center of the diagram are not necessarily the most central in terms of their number of connections to others”.

This also relates to another G.I.Science topic: visualization. Being able to visualize ones data is a powerful instrument to have when conducting science as it can reveal patterns and relationships that might not be evident. When the visualization is misleading, however, (ex. the Mercator projection and relative country size) problems can arise. Knowing of the problems that exist with the visualization and how to use it correctly is necessary for successful applications of both G.I.Science and S.N.A.



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