Learning Outcomes:

The course aims are:
• To introduce students to graph theory and social network analysis
• To explore and visualize social variables that can be defined in terms of relationships as opposed to independent attributes
• To provide an overview of physical social networks and their characteristics such as homophily, small-world properties, and clustering
• Similarly, to provide an overview of the characteristics of online social networks such as scale-free distributions, polarization, algorithmic ranking, and echo-chamber communication
• To provide an introduction to social network analysis tools and software, such as statnet, igraph, Gephi, and UCINET
• To develop an understanding of how and under which circumstances online social networks can be mapped onto offline social networks

Knowledge and understanding:
On successful completion of this module, you will be expected to be able to:
• Describe the fundamentals of graph theory and explain the role of relationships in studying social behaviour
• Be familiar with seminal work in social network analysis and its applications
• Have the capacity to explain how different network layouts can be transformed or converted into one another, e.g., edge lists, matrices, tables, lattices
• Be able to describe and debate key issues cutting across online and offline social networks
• Evaluate the limitations and challenges involved in mapping online to offline social networks

Learning outcomes:
Upon completing the module, you will be expected to be able to:
• Discuss and debate current issues of social network analysis and social media
• Demonstrate clear written communication, oral communication, and presentation skills
• Present, evaluate, and interpret relational data in connection to communication, sociological, and spatial theories
• Make reasoned judgements and demonstrate a capacity for independent thinking
• Access and utilise research resources drawing from social network analysis, sociology, communication, and spatial statistics in relation to social media and offline social networks
• Critically describe the relationships between online and offline social networks, and how these sources of social activity can be mapped onto each other
• Be familiar with methods to collect, retrieve, visualise, and analyse social network data online (e.g. social media platforms) and offline (e.g. institutional or intergroup affiliation)
• Undertake accurate reading and clear written communication
• Show self-reliance and the ability to manage time and work to strict deadlines
• Evaluate complex arguments to critically assess practice and procedure

Indicative Module Content:

Week 01: Introduction to Social Network Data
Week 02: Introduction to Social Network Analysis
Week 03: Measures of Centrality
Week 04: Homophily and Communities
Week 05: Diffusion and Contagion
Week 06: Local and Digital
Week 07: Social and Spatial Networks
Week 08: Readership Online and Offline
Week 09: Physical and Online Networks
Week 10: Mapping Online to Offline Networks
Week 11: Protest Coordination Online and Offline
Week 12: Mapping Social Data

Student Effort Type	Hours
Lectures	24
Total	24

Not applicable to this module.

Not applicable to this module.
 

Assessment Strategy  

Description	Timing		Component Scale		% of Final Grade
Essay: Essay between 2700-3300 words (excluding diagrams, graphs, images, or bibliography). A 3000-word essay is roughly equivalent to 6 pages single-spaced or 12 pages double-spaced	Coursework (End of Trimester)	n/a	Graded	Yes	50
Continuous Assessment: 200-word reaction papers addressing the weekly core readings of weeks 02-11 (between 270 and 330 words). This set of weekly reaction papers should be emailed to the Module Leader by Friday EOD.	Throughout the Trimester	n/a	Graded	Yes	50

 

Resit In	Terminal Exam
Summer	No

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment

Not yet recorded.

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Spring
Lecture	Offering 1	Week(s) - 20, 21, 23, 24, 25, 26, 29, 31, 32, 33	Mon 10:00 - 11:50