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Introduction to Social Network Analysis (with R)

Course Dates and Times

Monday 14 – Friday 18 February 2022
2 hours of live teaching per day

14:00 - 16:00 CET

VIR This is a virtual course

Filip Agneessens, course instructor for Introduction to Social Network Analysis (with R) at ECPR's Research Methods and Techniques
Filip Agneessens

filip.agneessens@unitn.it

University of Trento

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.

Purpose of the course

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.

ECTS Credits

3 credits Engage fully with class activities

Instructor Bio

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).

 

Key topics covered

Day 1 Types of networks and visualisation with R

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:

  • Introduction
  • Different types of social networks (directed versus undirected, valued versus binary, one-mode versus two-mode, positive and negative ties)
  • Types of network ties and how they are collected
  • Basic ways to visualise networks with R
  • Basic measures: density, centralisation and degree centrality

Homework Extra exercises for visualising networks with R and calculating basic measures.

Day 2 Centrality measures and network theories

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':

  • Closeness centrality
  • Betweenness centrality
  • Eigenvector and beta-centrality
  • Social capital
  • Social network theory (e.g. strength of weak ties, structural holes, Simmelian ties)

Homework Calculate the centrality of nodes in a network using R and interpret their meaning.

Day 3 Ego-network measures and nodal characteristics

We explore different measures of position that incorporate nodal characteristics and discuss their meaning:

  • Different measures of homophily
  • Measures of diversity
  • Measures of individual resourcefulness

Homework Calculate different ego-network measures on example datasets.

Day 4 Network structures and subgroups

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:

  • Identifying subgroups in a network
  • Blockmodelling
  • Structural equivalence
  • Regular equivalence

Homework Use R to identify subgroups and structural equivalence.

Day 5 Two-mode networks and introduction to statistical models for social networks

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:

  • Nodal level analysis and permutation tests
  • Exponential random graph models for dyadic analysis
  • SAOM / SIENA models for longitudinal analysis

Homework Analyse two-mode network data.

How the course will work online

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.

Online courses

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

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.

Disclaimer

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.

Day Readings
Monday

Further reading:

  • Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892-895.
  • For more on types of networks and collecting data: Agneessens, F., & Labianca, G. J. (2022). Collecting survey-based social network information in work organizations. Social Networks, 68, 31-47.
Tuesday

Further reading:

  • Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55-71.
  • Agneessens, F., Borgatti, S. P., & Everett, M. G. (2017). Geodesic based centrality: Unifying the local and the global. Social Networks, 49, 12-26.
Wednesday

Further reading:

  • “Chapter 8. Local Node-Level Measures”, In: Borgatti, Everett, Johnson and Agneessens (2022) Analyzing social networks with R. Sage.
Thursday

Further reading:

  • Doreian, P., Batagelj, V., & Ferligoj, A. (2005). Generalized blockmodeling (No. 25). Cambridge university press.
  • Ferligoj, A., Doreian, P., & Batagelj, V. (2011). Positions and roles. The SAGE handbook of social network analysis, 434-446.
Friday

Further reading:

  • Agneessens, F., & Everett, M. (2013). Special issue on advances in two–mode social networks [Special issue]. Soc. Networks, 35(2).
  • Robins, G., P. Pattison, Y. Kalish, and D. Lusher (2007). On exponential random graph models for cross-sectional analysis of complete networks: An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2): 173-191.
  • Lusher, D., J. Koskinen, and G. Robins (eds.) (2013) Exponential Random Graph Models for Social Networks. Structural Analysis in the Social Sciences. New York: Cambridge University Press.
  • Snijders, T.A.B., G. van de Bunt, G., and Ch. Steglich (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32: 44-60.
  • Agneessens, F. (2019). Dyadic, nodal and group-level approaches to study the antecedents and consequences of networks: Which social network models to use and when. In The Oxford Handbook of Social Networks. Oxford University Press.