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Monday 22 ꟷ Friday 26 March 2021
2 hours of live teaching per day
10:30-12:30 and 14:30-16:30 CET
This course provides a highly interactive online teaching and learning environment, using state of the art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and capped at a maximum of 16 participants so that the teaching team (the Instructor plus one highly qualified Teaching Assistant) can cater to the specific needs of each individual.
This course is designed to enable theory-driven research questions with scalable computational tools and empirical data, for researchers interested in conducting applied social network analysis in various social scientific disciplines.
3 credits Engage fully with class activities
4 credits Complete a post-class assignment
Silvia Fierăscu holds a PhD in Comparative Politics and Network Science from Central European University.
Her research focuses primarily on quality of governance, political-business relations, and statistical analyses of network data.
Silvia is involved in various interdisciplinary projects, translating complex problems into real-time applications for organisational management, political communication, and better governance.
For the live sessions, we start with a review of network analyses with social impact, theories of tie formation and mechanisms at work in networked phenomena (popularity, brokerage, reciprocity, transitivity, assortativity, preferential attachment, clustering, etc). We then make the transition from research questions and theory to appropriate data and research design.
We connect network structures and processes with social functions (measures at the network level; null models and rewiring) test how preferential attachment works and the empirical implications of the mechanism in different contexts (inequality, competition, influence, etc).
We focus on group formation theories and measurement techniques (measures at the community level; clustering, transitivity, community detection and motif discovery algorithms), test how cohesive groups form in different contexts and what implications clustering has for community building, belief reinforcement and change, etc.
We connect actors’ network positions with importance and roles (measures at the individual level ꟷ centrality measures), test how the mechanism of brokerage works in different contexts and the implications it has for strategic behaviour (structural holes, boundary spanners, innovation, gatekeeping, etc).
We wrap everything up into a discussion on complexity theory (emergent phenomena and unintended consequences), regression models for networks (ERGMs & SAOMs), opportunities and challenges in network inference (social selection vs social influence) and a short showcase of the measurable impact of SNA solutions in policy-making.
This course is the perfect opportunity to make the best out of blended learning and student-centred teaching using digital technologies. Its design has everything you need for an enriching and empowering learning experience:
Pre-course independent materials to prepare you for the live sessions
Live sessions with dynamic content
Independent class participation
The digital tools we will use (Slack, Google Colab, Classroom, Drive, Zoom, GitHub) are designed to create an engaged, long-term community of learners, and should give you support to rely on long after this course has ended.
The technical and project management skills and instruments you will learn and use are an added bonus to your substantive learning experience.
None for this class. We welcome researchers and practitioners with any disciplinary background or work experience. All you need to know prior to the class will be provided in pre-recorded videos (e.g. software tutorials) or available for consultation (e.g. readings, datasets, etc).