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Monday 14 – Friday 18 February 2022
2 hours of live teaching per day
14:00 - 16:00 CET
VIR This is a virtual course
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 can cater to the specific needs of each individual.
This course introduces core ideas of social network analysis, covering theory and methods. It is designed for those relatively new to social network analysis, or those who want an overview of the main concepts and ideas in the field.
We will focus primarily on complete network data, such as network data among students in a school, or connections between organisations, although we'll also pay some attention to ego-network / personal networks. We will introduce the use of R to analyse networks.
3 credits Engage fully with class activities
Filip Agneessens is an Associate Professor at the Department of Sociology and Social Research, University of Trento.
He has published on a diversity of topics related to social networks, including measures of centrality, statistical models, ego- networks and social support, two-mode networks, negative ties, multilevel networks and issues related to data collection. He has also applied social network analysis to understand the antecedents and consequences of interactions among employees, and in particular within teams. Together with Martin Everett, he was a guest editor for a special issue on “Advances in Two-mode Social Network Analysis” in the journal Social Networks, and together with Nick Harrigan and Joe Labianca he guest-edited a special issue on “‘Negative and Signed Tie Networks”’. He has taught numerous introductory and advanced social network courses and workshops over the last 15 years. Together with Steve Borgatti, Martin Everett and Jeff Johnson he co-authored the book “Analyzing Social Networks with R” (Sage, 2022).
We discuss basic ideas of social network analysis, identify different types of networks (e.g. one-mode versus two-mode) and discuss ways to visualise social networks with R:
Homework Extra exercises for visualising networks with R and calculating basic measures.
We discuss different measures of centrality, and when each could be useful. We also explore main theoretical arguments in the literature, including structural holes, the strength of weak ties, and Simmelian ties. We discuss concepts such as 'six degrees of separation' and 'small worlds':
Homework Calculate the centrality of nodes in a network using R and interpret their meaning.
We explore different measures of position that incorporate nodal characteristics and discuss their meaning:
Homework Calculate different ego-network measures on example datasets.
We turn to the overall network and focus on identifying subgroups in a network. We discuss different types of network structures, such as the core-periphery-ness of a network:
Homework Use R to identify subgroups and structural equivalence.
We focus on analysing two-mode networks, and discuss different types of statistical models for social network analysis. We provide a basic overview of current models for doing statistical analysis, and discuss what type of hypotheses these could answer:
Homework Analyse two-mode network data.
Each day involves two hours of synchronised teaching during which we discuss the core part. This is followed by autonomous homework (often involving R), which you can do in small groups. We will provide answers, and discuss them briefly the next day. We will also make further reading on the topic available.
This is an online only course. No prior knowledge is required, but please install RStudio on your computer.
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.
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Monday |
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Tuesday |
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Wednesday |
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Thursday |
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Friday |
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