MSc Social Network Analysis

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

• MSc (full-time)
  UK students (per annum): £14,200
  International, including EU, students (per annum): £26,300
• MSc (part-time)
  UK students (per annum): £7,100
  International, including EU, students (per annum): £13,400

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

International student CAS deposit

Self-funded international applicants are required to pay a deposit of £2500 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. Some applicants will be required to pay a higher deposit. More information on tuition fee deposits .

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

We know that student finance can be complicated. The links below provide further information to help guide you.

• Learn more about - student finance options for UK students .
• Learn more about - fees and finance for international students .
• Graduates of The University of Manchester and Manchester Metropolitan University can receive a 10% discount on their master's degree tuition fees as part of our Manchester Alumni Loyalty Discount scheme.

Students are strongly recommended to join this course only if they have prior training in social network analysis, such as SOCY60361 (Social Network Analysis) in semester 1 or the undergraduate course SOST30022 (Network analysis) in semester 2.  

The unit aims to:  

1. Present the rationale for statistical network modelling.  

2. Define network models.  

3. Introduce key statistical models for network analysis.  

4. Teach applying statistical modelling to empirical data.  

The course involves lectures and computer practicals. The lecture component provides theoretical and methodological frameworks for learning about the analysis of social network data and the key pathways from theory to subjecting research questions to empirical scrutiny. The practicals are linked to the lectures and serve to give a concrete and hands-on perspective on the material taught. Furthermore, the practicals give students training in specific methodologies and embed practical skills. The practicals have an immediate goal of equipping students with the necessary skills and knowledge to complete the assignment. Canvas resources are used to enable students to access teaching data and data sources. Students are also provided with video materials of lectures and software tutorials.

A1. Critically engage with the theoretical foundations of network analysis and use them to formulate empirical questions relevant to network analysis. 

A2. Design and develop network studies. 

A3. Understand the variety of network data. 

A4. Assess the applicability of network-analytical techniques to a given dataset. 

A5. Understand the motivation behind the statistical modelling of networks. 

A6. Critically understand and evaluate network-analytical research, reflect upon related methodology in a theoretically-informed way. 

A7. Understand network-analytical research questions in multidisciplinary contexts, and efficiently operationalise them. 

B3. Critically discuss network-analytical literature applying complex statistical models and identify the most appropriate statistical model for a given research problem. 

B4. Examine network structures using descriptive measures, and statistically model the mechanisms for social network structuring. 

B6. Choose appropriate techniques for network data visualization. 

B7. Report results of social network analysis in written form. 

C2. Design and develop tailored network-analytical research projects on a variety of real-world problems. 

C4. Collect, manage, and analyse online and offline datasets, and efficiently approach network data analysis and management. 

C5. Produce state-of-the-art network data visualizations. 

C6. Be proficient in network analysis software.

D1. Develop new or enhanced skills to identify and use diverse social network data and use such data to inform research projects and interventions in a variety of contexts. 

D2. Understand and mediate multidisciplinary environments and liaise across different intellectual and practical contexts involved in network studies

D3. Work collaboratively, both face-to-face and online, on network-analytical projects. 

D4. Accurately and effectively work with numbers and use advanced computational software for network analysis. 

Written assignment (essay): 100%. 

The word count must not exceed 2000 words. 

The essay must include a (1) network visualization and tables with (2) descriptive statistics, (3) statistical model and goodness of fit test, (4) interpretations of 1-3.

Feedback available via Turnitin

Essential:  

Borgatti S., Everett M, Johnson J. (2018). Analysing Social Networks 2nd Ed, Sage, London  

Lusher D., Koskinen J., and Robins G. (2013). Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press  

Additional:  

Hanneman R.A. and Riddle M. (2005). Introduction to Social Network Analysis. Available at https://faculty.ucr.edu/~hanneman/nettext/  

Robins G. (2015). Doing Social Networks Research: Network Research Design for Social Scientists. Sage.  

Wasserman S. and Faust K. (1994). Social Network Analysis, Cambridge University Press 

For Information and advice on Link2Lists reading list software, see:  

http://www.library.manchester.ac.  

uk/academicsupport/informationandadviceonlink2listsreadinglistsoftware/  

Scheduled activity hours 30 hours (mixed lecture/tutorial format)