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Please note: This course is only open to registered PhD students, with preference given to those at London-based institutions. If you show up to this course without being registered, you will be denied entry. You'll be asked to provide your student number and other information for registration on this site and need to bring your student card when you attend the first session of the course. It is essential that you prepare for the course, doing any required readings and preparation mentioned below. If after booking you later find you cannot attend this course, please cancel your place as soon as possible using this Eventbrite system.
Course convenors: Dr Eelke M. Heemskerk & Dr Frank W. Takes
25 October 2016, 9am-4pm- 2.87 Franklin Wilkins Building
26 October 2016, 9am-4pm- 2.84 Franklin Wilkins Building
Refer to KCL Waterloo campus detail map here: http://www.kcl.ac.uk/campuslife/campuses/download/2014/waterloo-detail-map-A4.pdf
This course familiarises PhD students with the main network theories in social science and develop basic skills in network analysis. The course starts with an overview of social network theory and basic concepts in SNA. We contextualise SNA within the social sciences, exploring differences between the focus on social relations and approaches that focus on individual attributes. Students will then be presented with examples of important contributions that SNA has made to our understanding of human society. Finally, fundamental concepts used to describe network topologies will be introduced. From this we move to an introduction in several network analysis methods and measures. Key issue here is how to find the best match between methods and techniques on the one hand, and your research question and type of data on the other. This also addresses issues such as ‘what type of empirical data is suited for SNA?’ and ‘how does one collect and prepare data for analysis?’
Students will become familiar with social network theory and analysis as a practical set of research instruments to empirically investigate the theoretical questions. They will learn how to analyse network structures (for instance centrality; community detection) and visualize these networks using Gephi software package.
Link to full course outline here: http://www.kcl.ac.uk/study/pg/school/dtc/assets/KISS238.pdf
Students should have read the required chapters of Scott (see course outline). For the hands-on network analysis part of the course we use Gephi. If you prefer, you can install this on your own computers before the short course.
http://gephi.github.io/users/download/ (open source)