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Analysis of Social Network Composition

The author of this article, Drew Prindle, discusses the development and evolution of social networking over the past few decades. While the author does not go into intricate technical details regarding the social networks and their structural composition, one can use his/her own knowledge of the properties of networks to analyze and explain the growth and popularity of social networking over time.

Shortly after the birth of the internet, the first true network to appear was based off of Bulletin Board Systems (BBS). This first network could be modeled as groups of smaller, isolated components (clusters), of a much larger system (this is because the “edges” of these networks were telephone lines, and long distance calling rates limited peer-to-peer interaction within local areas). The model follows the rule of Large Real Networks – that is, large real-life networks almost always have 0 or 1 giant components (clusters). In the case of the BBS, there are 0 giant components – this changed, however, by the mid-1990’s, when the internet started to become more mainstream and accessible, and by this time, the internet network system could be more accurately modeled as having 1 giant component.

Prindle goes on to discuss more modern-day social networking sites, such as LinkedIn (which has approximately 175 million users), Myspace (roughly 300 million users), (540 million users in total), and Facebook (which is just short of 1 billion users). Applying network theory here, one can see certain similarities. If each of these social networks is modeled separately as a network of nodes and edges, they would all have the users as nodes. However, the edges in each network differ. LinkedIn’s edges would be business/work connections. For Myspace, the edges would be “friendships,” and for, it would be school-based connections. However, it can be argued that Facebook’s “friend” connections/edges are based off of multiple factors (including all the ones previously mentioned). When adding friends on Facebook, one can use search filters such as similar places of education, business, and local area to properly determine connections. This is a logical explanation for why Facebook is currently the largest and most comprehensive social networking site, which links users to friends, classmates, co-workers, relatives, etc. LinkedIn, MySpace, and Classmates can be modeled as networks of work, friendship, and scholarship, respectively, while Facebook seems to a (social) network, that is formed as an intricate fusion of multiple, separate networks, that will overlap based on user/cluster similarities (i.e. if many students who go to a certain school also go on to work at the same company). As we all know, Facebook is essentially the “dominant” social network today and while its setup and user interface add to its success, Facebook’s true claim to fame is how users connect. In theory, according to Prindle and based on a thorough analysis of the article, the future of social networking lays in the hands of open-source programmers and online communities. These networks should be able to allow users to connect by integrating other major network systems (i.e. social gaming networks, internet forum groups, and even analysis of the user’s other social network site data), that Facebook has yet to implement, in order to gain an edge over the competition.



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