M Summer. School on Network Analysis for Social Sciences 2025
15-26 September 2025 - The 'M Summer. School on Network Analysis' offers three courses, from introductory to advanced/statistical modelling to researchers and professionals with a social science focus.
The three in-person week-long courses are taught by members of the Mitchell Centre for Social Network Analysis – a leading cross-disciplinary research group in the development and application of social network analysis techniques, located in the School of Social Sciences at The University of Manchester.
Prolonged deadline: 14 July 2025
Teachers are Filip Agneessens, Nikita Basov, Nick Crossley, Tomáš Diviák, Martin Everett, Michael Genkin, and Philip Leifeld.
Each of these courses consists of a total of 30 contact hours (9.30am to 4.30pm from Monday to Friday – either between 15-19 or 22-26 September) and will take place at The University of Manchester (in the Arthur Lewis Building).
Fees for a one-week course:
- Academics from A economies: £600,
- Students *and* B and C economies academics: £450,
- Industry: £1000 per week.
The Mitchell Centre has adopted a dual fee structure based on the World Bank’s classification of economies into three categories (A,B,C) as adopted by International Sociological Association (ISA). For more information about this classification visit the International Sociological Association website and contact us if you have any questions.
There is a £100 discount for booking two courses - please contact us for further details.
"This was one of the best social network analysis courses I've attended ... I'm now using many of these tools in my dissertation research" - Natasha W, USC
COURSE 1: Introduction to Network Analysis for Social Sciences with UCINET
15-19 September 2025
This is a core introductory course on network analysis for social scientists. The course covers the main network-analytical concepts, methods, and data collection/processing techniques for social sciences. The course is largely based around the use of UCINET point-and-click software and is broadly accessible.
The course is hands-on, allowing participants the opportunity to gain experience in analysing real-world social network data, and also offers participants the option to discuss their own projects in network analysis with relevant experts from the Mitchell Centre.
The course covers the following topics:
- Introduction to SNA
- Introduction to UCINET
- Transformation and network visualization
- Local node-level measures
- Group-level measures
- Centrality measures
- Two-mode networks
- Subgroups/community detection
- Data collection (Choose among: Survey Data, Online Data* or Archival Data)
- Projects
No prior knowledge of network analysis or quantitative methods is required for this course. Basic computer usage skills are the only requirement for this course.
Practical information
The course is tailored to UG, PG and PhD students, as well as more senior staff and academics with a social science background, and industry professionals, from around the world.
Software: UCINET (time-limited freeware). Participants are to bring their own laptops and have UCINET installed. Mac users will need to use a Windows emulator. (The optional “Online data collection” class will require some very basic R).
Core reading: Borgatti S., Everett M.G., Johnson J. and Agneessens, F. 2024. Analyzing Social Networks (3rd Ed.). London: Sage.
Participants are encouraged to also take a subsequent course on Statistical Network Modelling for Social Sciences, 22-26 September 2025.
"I’m using everything I learnt to develop a fellowship application" - Caitlyn D, Cardiff University
COURSE 2: Network Analysis for Social Sciences with R
15-19 September 2025
This course offers an in-depth overview of concepts and measures in social network analysis.
This course is (slightly) more technical than the “Introduction to Network Analysis for Social Sciences with UCINET” and will be using R (instead of UCINET). No prior knowledge of social network analysis or R is required, but a basic knowledge of social network analysis and of quantitative methods can be an advantage.
The course covers the following topics:
- Introduction to R and R packages for SNA
- Importing network data in R, basic manipulation of network data and visualization
- In-depth overview of centrality measures (including induced centrality)
- Measures of position with attributes (homophily, diversity, resourcefulness) and structural holes (such as constraint index)
- Equivalence and blockmodeling
- Advanced R: multiplex and signed networks, or two-mode networks
- Advanced R: missing data, or discourse analysis
- Data collection (Choose among: Survey Data, Online Data or Archival Data)
- Short introduction to statistical models for SNA
In addition, the course offers the participants the opportunity to discuss their own projects with relevant experts from the Mitchell Centre.
Practical information
The course is tailored to PG, PhD, students and academics of any social science background, as well as industry professionals, from around the world.
Software: R and specific packages, such as sna, igraph, xUCINET and manynet. Participants are to bring their own laptops and have R installed prior to the start of the course. Information about how to install R will be provided before the start of the course.
Core reading: Borgatti S., Everett M.G., Johnson J. and Agneessens, F. 2022. Analyzing Social Networks with R. London: Sage.
Participants are encouraged to also take a subsequent course on Statistical Network Modelling for Social Sciences, 22-26 September 2025.
"As someone without a statistical background or knowledge of the field before taking the course, I felt welcomed and confident in my understanding of SNA after finishing the course" - Anna F, University of Manchester
COURSE 3: Statistical Network Modelling for Social Sciences
22-26 September 2025
This course offers an in-depth overview of statistical models for social network analysis.
The course covers some of the core statistical methods, including Exponential Random Graph Models (ERGMs), Stochastic Actor-Oriented Models (SAOMs/SIENA) and Relational Event Models (REMs). In addition, we will cover tERGMs, autoregressive models (ALAAMs) and ERGMs/SAOMs for two/mode and multi groups (multilevel). The last day participants will be able to choose between advanced longitudinal network models, or semantic and socio-semantic network modelling with automap and mpnet.
The course is hands-on, offering participants the opportunity to analyse real social network data. In addition, the course offers the participants the opportunity to discuss their own projects with relevant experts from the Mitchell Centre.
For most of the analysis, we will be using R (as well as MPNet). No prior knowledge of R is required, but basic knowledge of social network analysis and quantitative methods is recommended (see also the course in the first week of the Summer School).
Practical information
The course is tailored to PG, PhD, students and academics of any social science background, as well as industry professionals, from around the world.
Software: R and specific packages, such as “ergm” and “rSIENA”. Participants are to bring their own laptops and have R installed prior to the start of the course. Information about how to install R will be provided before the start of the course.
Basic background reading:
Borgatti S., Everett M.G., Johnson J. and Agneessens, F. 2022. Analyzing Social Networks with R. London: Sage. (Chapters 14-15).
Borgatti S., Everett M.G., Johnson J. and Agneessens, F. 2024. Analyzing Social Networks. London: Sage. (Chapters 14-15).
Core reading:
Lusher, D., J. Koskinen, and G. Robins (eds.) (2013) Exponential Random Graph Models or Social Networks. Structural Analysis in the Social Sciences. Cambridge University Press.
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.
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.