Scott Weingart’s, “Demystifying Networks” discusses the basics of networks and the power of network analysis—when used correctly. Creating a network visualization can be done for most projects, but using the correct methodology for these visualizations is incredibly important as well. Because networks are “any complex, interlocking system,” which reduces to “stuff and relationships,” network analysis can illuminate relationships in large sets of data. Online, social networks are formed around almost anything, from intellectConnect to Goth Passions to Facebook. The objects studied within these networks are considered “interdependent rather than independent” from one another and require “relationships [in order for researchers] to understand” what’s going on within the network. The “stuff” within the network defines what kind of network it is. For example, the “stuff” within Facebook is people and these individuals have different attributes (such as DOB, Location, High School, etc.) and create various multimodal networks. Every individual has a relationship with someone else within Facebook, creating a relationship, and each type of relationships have a type of edge, “defined…by the nodes they connect.”
However, can relationships be seen even with individuals are not within the Facebook network? A study, “One Plus One Makes Three (for Social Networks)” shows that connections can be deduced between members and non-members through member’s confirmed email contacts. The study found out that:
Social network platforms…have direct access to two different sets of relationships: on the one hand, the mutually confirmed contacts between platform members; and on the other hand, their members’ unilateral declarations of their acquaintance with non-members. The edges in both are an abstraction and a subset of the edges in the latent social graph… with the help of machine learning, social network operators can make predictions regarding the acquaintance or lack thereof between two non-members with a high rate of success…These are the first results on the potential of social network platforms to infer relationships between non-members.
This study exemplifies the power of social network analysis, as even those i
ndividuals who have chosen to not participate in a social network (in this case Facebook) can be inferred by machine technology as being connected to a member of said network. No matter what, individuals are unable to escape social networks, for being an individual requires the need to engage with at least one person a day, and these interactions inevitably lead to connections. Even when not participating on a social networking site, these connections can still be predicted. There is no safety from the net!
Horvát, Emöke-Ágnes, Michael Hanselmann, Fred A. Hamprecht, and Katharina A. Zweig. “One Plus One Makes Three (for Social Networks).”PLOS ONE. PLOS ONE, 6 Apr. 2012. Web.
Scott Weingart, “Demystifying Networks.” Web.