Social Networking

Social Network Analysis

Originally the term social network was used as a metaphor to connote relationships between members of social systems at all scales from the individual to the international. However, especially during the second half of the 20th century, social network analysis became its own paradigm with an impressive academic literature, the development of theories, concepts, debates, methodological tools, and researchers spanning diverse disciplines and multidisciplinary academic areas, ranging from sociology to communications, political economy, biology, geography, information science, business, political sociology, social psychology, and computer science. The current use of social network analysis refers to the exploration of structural ties and how they affect individuals, their relationships, and their environments. Research conducted under this analytical framework has had enormous implications for our understanding of social structures and has provided valuable insights into how social networking actually works.

Small World

First, there is the idea of a small world phenomenon, describing the chain of acquaintances to link one person to any other on the planet, the six degrees of separation, or the number of connections any point in a network has to other points, as described by Stanley Milgram in 1967. Currently, the World Wide Web exhibits small world phenomena making most websites reachable through a small number of links. Further, Mark Granovetter famously found, in what he called the strength of weak ties, that weaker ties are stronger and more important for acquiring new information, as members of closed systems or cliques would have to go outside their immediate cluster to find new information. His idea was proven by Duncan J. Watts and Steven H. Strogatz, who demonstrated that networks from both the natural and the man-made world exhibit a small world property.

Network Characteristics

One of the most significant network characteristics is a predictable topology in a variety of networked phenomena, as the distribution of links into and out of nodes on the network follows a power law. In 1999, Albert-László Barabási and Réka Albert expressed the idea that there is a low probability that any node will be highly connected to other nodes and a large probability that a large number of nodes will be connected not at all or very loosely. In terms of the web, power law distribution means that there is a long tail: A large number of sites have few or no links, while a small number of sites have a large number of links. They explained this with the term preferential attachment, describing that new nodes prefer to attach to already established nodes. Networks that follow a power law distribution tend to be more robust and adaptable to environmental disturbances. Concepts that have gained currency in organizational studies especially are intervisibility, the extent to which the nodes in a network are communicating, and the social capital gained from bridging structural holes, linking people who are not linked together. Other important insights include the self-organizing principle, whereby without a formal hierarchy and without centralization through collaborative peer-filtering processes, nodes in a networked community are successful in exploiting and integrating knowledge. Open-source software communities are often used as an example of successful peer production and a new model for business development though digital networks. Participating individuals are not motivated by money, but by prestige, reputation, and knowledge or skills gained from participating in collaborative networks.

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