Statistical Network Modelling for Social Sciences
Date:
22/09/2025 - 26/09/2025
Organised by:
University of Manchester, Mitchell Centre
Presenter:
Filip Agneessens, Nikita Basov, Nick Crossley, Tomáš Diviák, Martin Everett, Michael Genkin, and Philip Leifeld.
Level:
Intermediate (some prior knowledge)
Contact:
SoSS Research Operations
soss-research-operations@manchester.ac.uk
Map:
View in Google Maps (M13 9PL)
Venue:
Arthur Lewis Building,
University of Manchester
Oxford Road
Manchester
Description:
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.
Cost:
Academics from A economies - £600
Students and B/C economy academics - £450
Industry - £1,000
Website and registration:
Region:
North West
Keywords:
Data Collection, Data Quality and Data Management , Quantitative Data Handling and Data Analysis, Social Network Analysis, Interaction analysis
Related publications and presentations from our eprints archive:
Data Collection
Data Quality and Data Management
Quantitative Data Handling and Data Analysis
Social Network Analysis
Interaction analysis