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Social network analysis is a method of inquiry that focuses on relationships between subjects such as individuals, organisations or nation states. However, it has also been applied to analysis relationships between objects as diverse as the Internet, scientific papers, organisms, and molecules.
Social network has been a subject of increasing interest in the sciences as well as social sciences recently. Last year alone, Thomson ISI (Institute for Scientific Information) recorded 420 papers published across the sciences, social sciences, arts and humanities disciplines. This figure excludes papers published in Social Networks journal.
These materials aim to help beginners to appreciate and use social network analysis in their own work and pursue further developments on their own. There are two complementary and useful books that can be recommended at this stage i.e. Wasserman and Faust (1994) and Scott (1991) and this material will not in anyway replace the need to consult these and other references listed below. However, the material is designed with the objective of helping readers through learning by doing analysis themselves. It can be covered in about three sittings each less than an hour. Having a printed copy to hand can help in this respect.
The most widely used software for social network analysis is UCINET and it is now in its sixth version. It is available for a small license fee for academic use from Analytic Technologies. For this project, however, we use Pajek, available freely for academic use from its authors' site. This software has been continually developed and has only recently reached version 1. It has been designed to analyse and visualise large network in the order of thousands vertices. It also has the facility to link with popular data analysis packages such as SPSS and R by providing its data to be used in those packages. Its most acute weaknesses is the steep learning curve required to use it stemming from the lack of documentation (Huisman and van Duijn, forthcoming). In this respect, these materials help to alleviate this weakness.
This project has been supported by the ESRC Research Methods Programme, Projet number H 333 25 0061 Social capital and consumption: promoting network analysis.
© G Tampubolon - 17 December 2004