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Introduction to Social Network Analysis

Module description

The purpose of the module is to introduce you to the topic of social network analysis. Conceptually, this is different than social networking platforms like facebook and twitter; social network analysis is a way of looking at social data that focuses on relationships between or among social entities. In the social sciences, we frequently find that the structure of social relationships is crucial for understanding any number of processes, trends, and outcomes like why people joing social movements, which individuals get social or financial opportunities, and how organisations collaborate to change policy. This course presents an introduction to various concepts, methods, and applications of social network analysis drawn from the social and behavioral sciences.

It is expected that you will have a working knowledge of the R programming language before starting this course, although review and assistance will be provided.

Module aims - intentions of the module

You will learn about the theories of social networks and how these ideas impact our understanding of other social science topics like political engagement, social capital, and deviance. We also discuss motivations for using social network analysis and the strengths and weaknesses of this approach in a variety of social science contexts. Using a combination of lectures, practical demonstrations and assignments, this module aims at developing your skills in the analysis and presentation of relational data. Specifically, you will learn multiple ways of formulating social network hypotheses and testing them using a combination of descriptive measures and inferential statistics. The course is taught using the programming language R. This course is only suitable for students who are either comfortable programming in R or currently learning R.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. recognize and evaluate in writing the diversity of specialized techniques and approaches involved in analysing social network data in political science, sociology and criminology;
• 2. use statistical analysis to test a social networks hypothesis;
• 3. show ability to present and summarize analysed data in a coherent and effective manner;
• 4. demonstrate acquired skills, confidence and competence in a computer package for statistical analysis (the SNA package in R).

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 5. understand and use the tools and techniques of social network analysis for political and social data;
• 6. use social network evidence to empirically evaluate the (relative) validity of political, sociological and criminological theories and hypothesis;
• 7. construct well thought out and rigorous data analysis, tables and reports for both written and oral presentation;
• 8. examine relationships between theoretical concepts with real world empirical data.

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 9. demonstrate an ability to study independently;
• 10. use IT – and, in particular, statistical software packages - for the retrieval, analysis and presentation of information.

Syllabus plan

Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following topics:

• Topic 1: Introduction
• Topic 2: Centrality
• Topic 3: Measures of Network Structure
• Topic 4: Social Capital
• Topic 5: Block Models and Structural Equivalence
• Topic 6: Basic Network Statistics
• Topic 7: Brief overview of advanced techniques

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Indicative learning resources - Basic reading

Borgatti, Stephen P., Martin G. Everett, and Jeffrey C. Johnson. Analyzing social networks. SAGE Publications Limited, 2013.

Scott, John, and Peter J. Carrington. The SAGE handbook of social network analysis. SAGE publications, 2011.

Bonacich, P., and Lu, P. (2012). Introduction to Mathematical Sociology

Borgatti, S. P., Mehra, A., Brass, D. J., and Labianca, G. (2009). Network analysis in the social sciences. science, 323(5916), 892-895.

Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., and Tranmer, M. (2017). Social Network Analysis for Ego-Nets.

Indicative learning resources - Web based and electronic resources

Hanneman, Robert A. and Mark Riddle. 2005. Introduction to social network methods. Riverside, CA: University of California, Riverside (published in digital form at http://faculty.ucr.edu/~hanneman/)

Key words search

Social Network, Political Methods, Quantitative