Social network analysis

Social network analysis is a method of social research to collect and analyze social relations and social networks. This detection and analysis is also used in psychology eg part of organizational consulting and development processes.

History

The social network analysis has been used in its early forms in the 1930s. Her breakthrough achieved with the establishment of the block model analysis by the Harvard - structuralism, which culminated in the creation of a private research direction. With the advent of modern software applications at the beginning of the 1990s, this method has gained in science increasingly important and has enjoyed increasing popularity since then.

Metrics / analysis process

The network analysis uses several methods with which to analyze social networks and allow systematic and quantifying describe. Thus, the metrics can help to understand complex networks. Together, all the metrics that they are interested in the relative position of individual actors in a network and not to particular attributes / properties of the people.

  • Method for calculating centrality ( engl: Centrality ): These aim to identify the most important, most active and prominent actors in a network. It is commonly distinguished between degree centrality, and closeness centrality intermediate centrality of actors: Degree centrality (English: Degree): This measure presses of how many connections (relations) an actor to other actors of the network has. A distinction is made between, on the one actor outgoing and directed to an actor connections. The former are referred to as out -degree, the latter as in -degree. The degree centrality is sometimes not a good measure of the position of an actor in the entire network. Since only find the connections to other actors into account, actors with many connections as the central counted as actors who are at critical / key points of the network. So it need not be a disadvantage if you are connected with only two actors in a network instead of everyone, but offer these two for example, access to important information.
  • Between centrality (English: betweenness centrality ): This is expressed through which actors, for example, the majority of information is conveyed in a network, or through whom most of the communication takes place. Often actors combine with a high centrality between two se separate parts of a network. This would disintegrate with the omission of the actor as a link in two separate parts which have nothing to do with each other.
  • Proximity centrality (English: Closeness Centrality ): With this analysis method to measure not only the connections of an actor directly to nearby neighboring actors, but to all the actors of the network. Close centrality is defined as the average distance of a path to the other actors of the network.

Object of investigation

With the social network analysis can be a variety of different types of networks to examine For example:

  • Communication networks include the information or knowledge exchange between social actors.
  • Evaluation and feeling networks include friendships, relationships of trust, but also antipathy between actors.
  • Transaction networks describe the transfer of resources ( for example, workflow networks).

Analysis software

  • Pajek - program for the analysis and visualization of networks, which was developed at the University of Ljubljana.
  • UCINET - software package for the analysis of social network data, which was developed by Linton Freeman, Martin Everett and Steve Borgatti.
  • Gephi - open source software for analysis and visualization of networks
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