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Install this application on your home screen for quick and easy access when you’re on the go.
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Monday 3 ꟷ Friday 7 August 2020
2 hours of live teaching per day
Courses will be either morning or afternoon to suit participants’ requirements
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
o pre-recorded guided tours of software use and functionality
o guides to dataset hunting and data collection
live sessions with dynamic content
o conceptual discussions
o class exercises
o breakout room group activities
o one-on-one research problem consultations
o multimedia curated content
independent class participation
o practical homework assignments
o fun quizzes
o final projects on data and topics of choice
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).
Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.
Please check your course format before registering.
Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.
In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.