The readings this week really helped me to look at networks from an enriched, more detailed perspective. Research into networks deals with more than just personal attributes but with how people interact in relationships (Keim, 2011).
The subset of lettered nodes in the picture above represent relationships that contribute to the whole. The collection of these relationships provide us with social networks, where the whole is greater than the sum of its parts. For example, it is the network that gives us social structure to understand what the norms of society may be. Chances are, most of us follow these norms without even realizing because they are so ingrained into who we are.
At a more basic level, relationships can be thought of in the context of information exchange (Haythornthwaite, 1996). It is information between actors, or nodes that causes feelings of friendship, belonging and love to develop. This flow of information can determine new connections that form, and also can effect the network as a whole.
Over time, the consequences of this flow can significant. Take for example the idea of homophily (McPherson et al., 2001). People have a natural tendency to group with those like themselves. This can be seen in the relationships we have with friends, and in marriages. Homophily allows for the possibility of isolation or cluster formation because of selective interaction. As a consequence, perspectives can be narrower than they should be. Consider what has happened to our political system as noted below.
Contrary to generations past, homophily is making it more and more difficult to cross party lines. The effects of homophily as our society becomes more and more interconnected is an opportunity for further research. How will it affect our political system?
Social identity theory helps us understand the consequences of homophily. As it is explained in the video, an ingroup and outgroup is formed causing differences between the groups to be amplified from bias and discrimination. In effect, this causes the ingroup and outgroup to compete against one another rather than working together. (A sad set of circumstances for our political system.)
The two textbooks assigned for reading this week helped me to understand the semantics behind the component parts and representations of networks and how they can be applied to research various social networks. With my focus on health care administration, I especially appreciated how Robins used the example of snowball sampling to determine network boundaries. This method was applied to determine if a relevant, representative sample of decision makers was reached in the hospital setting.
As more and more hospitals implement electronic health records (EHR) increasing inter-connectivity between patients and physicians, as well as among physicians, I am curious to find out how this will ultimately affect social networks in health care. Will different departments started working more closely together? What are some of the long-term consequences. As of yet there are mixed reviews regarding EHR usage. This is also an area for future research.