RSS01.E2 Applied Social Network Analysis
Social scientists have never been more relevant than they are today - for the present and the future. With the increasing availability of data, the ever-growing performance of technologies, the fast-paced professionalization of research methods, and the huge social, economic, political and cultural problems around us, social scientists are uniquely positioned and trained to contribute, in tangible ways, to positive social impact.
Furthermore, in the current context of wide and deep societal transformation, amidst unprecedented levels of uncertainty and speed of change, social scientists have a timely opportunity to equip themselves with knowledge, skills and vision to understand and actively participate in building a future that is more inclusive, fair, innovative, sustainable, peaceful and prosperous.
Social Network Analysis is a comprehensive research framework that is strongly rooted in interdisciplinary theory, data, and practice. It looks at the interconnected world and it maps, measures, and identifies where it is hyperconnected and where it lacks connectivity; who are the key players, what is the role of groups and communities, and how information, engagement, resources, influence travel through social networks; and what all of these imply, for individuals, organizations, and societies at large. It seeks to pinpoint recurrent relational mechanisms and test network building and network disruption scenarios, to do better informed decision-making at all levels – personal, organizational, and in public policy.
|19 June 2023 - 23 June 2023|
Early Bird Regular: €895 (application deadline* April 1st)
|Scholarships and discounts||Find more information here|
*Your application is only completed when the course fee has been paid
|Course leader||Dr.Silvia Fierăscu|
|Level of participant||
|Admission requirements||In this class, you can choose to work with one or both network analysis software we cover, R (coding) and Gephi (point-and-click). We will cover basic R, the package ‘igraph’ in depth (‘network’ and ‘sna’ as supplementary), and the packages ‘ergm’ and ‘RSiena’ briefly. Previous familiarity with R is recommended. If you have no knowledge of R, you can familiarize yourself with the program, using resources we put at your disposal prior to the start of the class.|
|Mode of Study||On Campus|
|ECTS||2 or 4 Find more information here|