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Social Network Analysis

Vast swathes of our social interactions and personal behaviours are now conducted online and/or captured digitally. Thus, computational methods for collecting, cleaning and analysing data are an increasingly important component of a social scientist’s toolkit. Social Network Analysis (SNA) offers a rich and insightful methododolgical approach for uncovering and understanding social structures, relations and networks of assocation.


The following topics are covered under this training series:

  1. Fundamental Concepts - understand the fundamental concepts and terms underpinning social network analysis.
  2. Getting and Marshalling Data - learn how to collect and clean social network data.
  3. Techniques and Methods of Analysis - learn how to apply a range of analytical methods and techniques to derive substantive insights from social network data.


The training materials - including webinar recordings, slides, and sample Python code - can be found in the following folders:

  • code - run and/or download code using our Jupyter notebook resources.
  • installation - view instructions for how to download and install Python and other packages necessary for working with new forms of data.
  • reading-list - explore further resources including articles, books, online resources and more.
  • webinars - watch recordings of our webinars and download the underpinning slides.


We are grateful to UKRI through the Economic and Social Research Council for their generous funding of this training series.

Further Information

  • To access learning materials from the wider Computational Social Science training series: [Training Materials]
  • To keep up to date with upcoming and past training events: [Events]
  • To get in contact with feedback, ideas or to seek assistance: [Help]

Thank you and good luck on your journey exploring new forms of data!

Dr Julia Kasmire and Dr Diarmuid McDonnell
UK Data Service
University of Manchester


Materials associated with the Social Network Analysis training series



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