Course Overview
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. (Sage) with electronic copies available for the participants through University of Manchester’s library. This course can be attended in-person or online.
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. No prior knowledge of social network analysis is assumed for this course.
Course Aims
- Introduce the assumptions and main ideas underlying Social Network Analysis
- Explain how to describe and visualise networks using specialist software (UCINET)
- Explain and apply key concepts of Social Network Analysis (e.g. cohesion, brokerage).
- Provide hands-on training to use software to investigate social network structure
Your Course Leaders
Martin Everett is a Professor in Social Network Analysis at the University of Manchester and currently co-directs the Mitchell Centre for Social Network Analysis (with Nick Crossley). He has been a past president of the International Network for Social Network Analysis (INSNA) and is co-author of the software package UCINET and the Sage book Analyzing social networks. Martin regularly gives invited talks at major conferences and is co-editor of the journal Social Networks. He is a fellow of the Academy of Social Sciences and a Simmel award holder, the highest honour given by INSNA.
Nick Crossley is Professor of Sociology at The University of Manchester. His main work using social network analysis has focused upon music worlds, social movements and covert networks. He has also written extensively about 'relational sociology', a theoretical position that advocates a focus upon networks in sociology. His most recent book is Networks of Sound, Style and Subversion: the Punk and Post-Punk Worlds of Manchester, London, Liverpool and Sheffield, 1975-1976 (Manchester University Press).
Nikita Basov is a Senior Lecturer in Network Analysis at the University of Manchester. He has a PhD in Sociology from St. Petersburg University, 2009. From 2013 to 2022, he led Centre for German and European Studies, St. Petersburg University – Bielefeld University, supported by DAAD (German Academic Exchange Service) as Head of Science/Scientific Manager. In parallel, he worked at the Faculty of Sociology of St. Petersburg University, until leaving in 2022 as Lead researcher. Nikita’s main research interest is unveiling the mechanisms of meaning creation in the interplay between social and cultural (symbolic and material) structures. He advances multidimensional socio-semantic and socio-material network analyses, with a particular focus on combining qualitative data/methods with computational natural language processing and statistical network modelling.
Tomáš Diviák is a Presidential Fellow at the Department of Criminology and the Mitchell Centre for Social Network Analysis at The University of Manchester. He obtained his Ph.D. in Sociology at Charles University (Czechia) and University of Groningen (Netherlands) with a thesis called ‘Criminal networks: actors, mechanisms, and structures’. His research focuses on social network analysis (SNA), most prominently statistical models for network data, and on analytical sociology and criminology. He is interested in the application of SNA, mainly to criminal networks, but also to political, organizational, health-related, or historical networks.
Course Materials
Non-compulsory, but recommended reading:
- Scott, J (2000) Social Network Analysis: A Handbook. Sage.
Required materials:
- A copy of Analyzing Social Networks by Borgatti et al. (2018), which will be used throughout the course (electronic access will be provided for the duration of the course).
- Your own laptop on which you should download the UCINET and Netdraw software packages in advance. This can be done for a free period of time from the Analytictech website.
- It is useful for participants to have windows; Macs will need to have a PC emulator.
Course Timetable and topic overview
The course starts with the Methods@Manchester welcome talk at 1pm on Monday 1/7/24 and finishes at 12.30pm on 5/7/24
The delivery of the course will depend on whether you are attending the course in-person or online.
- In-person attendees will have a mixture of in-person lectures, demos and exercises.
- Online attendees will receive lectures in pre-recorded format to aid flexibility and two live sessions per day with the course tutors, alongside exercises to work through at their own pace.
Costs
In-person cost
- Full fee: £900
- Reduced/PGR fee: £600
Online cost:
- Full fee: £360
- Reduced/PGR fee: £240
Please note this course is alternatively available combined with the Mixed Methods in SNA course as a 4-day combined course. This can be booked directly and course details can be found here. This combined course is only available in-person.
Book your place
Places should be booked through the Methods@Manchester e-store by clicking the button below (payment by card only).
Bursaries
We are pleased to be able to offer a limited number of bursaries for Summer School applicants eligible for reduced fees.
The main bursary scheme is limited to PGRs from the North West Social Science Doctoral Training Partnership (NWSSDTP), but we also have a very limited number of bursaries for other applicants eligible for reduced fees who are facing exceptional circumstances.
Please see the main Summer School website (bottom of the page) for details of these bursaries and information about how to apply.
Any questions, please do not hesitate to contact us on methods@manchester.ac.uk
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