With its hyperlink structure, relational tables, friend lists and constant stream of information diffusion, network analysis is an obvious route to the analysis and understanding of the Internet’s dynamics. Indeed, the algorithms that power Google, Amazon, Facebook and Twitter are based in network science. Beyond the use of formal algorithms for network analysis are questions of societal import such as the consequence of the number and structure of Facebook friends; the overlap of personal network members on many media; the cascading behaviour of political activism; and the salience of identity in threaded conversations.
This course introduces social network analysis with particular emphasis on research design, data collection and analysis. We take a comparative approach to network topics, such as evaluating different measures of centrality, multiple approaches to clustering and variations on visualization, aiming not just to familiarize students with the basics of network analysis capture and analysis, but also to enable them to make informed choices for analysis based on research questions rather than default tools or outmoded conventions.
At the end of this course students should: have a familiarity with the basic terms and concepts of social network analysis; understand how differing network analysis metrics relate both to each other and to academic research questions; be able to describe how a network can be constructed from an online phenomenon; have a clear understanding of some of the various analytical tools used in network science; be able to construct and theorize a research question that employs social network analysis in order to address a specific topic related to human behaviour and collective dynamics.