Back to Events
Calls for Submission

Manchester Summer School in SNA

Monday, June 28, 2021 to Friday, July 2, 2021

Event Details

The Mitchell centre at the university of Manchester will be running two virtual sna classes in the summer school. Material will be recorded so participants can access material at a time that suits them.


28 June – 2 July 2021


1 Introduction to SNA


This is an introductory course, covering the concepts, methods and data analysis techniques of social network analysis.

The course is based on the book Analyzing Social Networks by Borgatti et al. (2018) and all participants are required to obtain a copy. The cost has been deducted from the normal course fee.

The course begins with a general introduction to the distinct goals and perspectives of social network analysis, followed by a practical discussion of network data, covering issues of collection, validity, visualization, and mathematical/computer representation. We then take up the methods of detection and description of structural properties, such as centrality, cohesion, subgroups and positional analysis techniques.

This is a hands-on course largely based around the use of UCINET software, and will give participants experience of analyzing real social network data using the techniques covered in the workshop.


2 Advanced SNA


This course provides an introduction to statistical analysis of networks. It aims to give a basic understanding of and working handle on drawing inference for structure and attributes for cross-sectional data.

A fundamental notion of the course will be how the structure of observed graphs relate to various forms of random graphs. This will be developed in the context of non-parametric approaches and elaborated to analysis of networks using exponential random graph models (ERGM). The main focus will be on explaining structure but an outlook to explaining individual-level outcomes will be provided.

The participant will be provided with several hands-on exercises, applying the approaches to a suite of real-world data sets using the software UCINET and R.


Further details are here (note the 2020 and not 2021!)