BSc Computer Science (Human Computer Interaction) with Industrial Experience / Course details
Year of entry: 2022
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Course unit details:
Advanced Social Network Analysis
|Unit level||Level 3|
|Teaching period(s)||Semester 2|
|Offered by||Social Statistics|
|Available as a free choice unit?||Yes|
The basic premise of this course is that the social world is relational. We can not ignore that we are influenced by people we know, have met and respect; ideas and allegiances are formed and maintained in social settings and organisations; not all people have equal opportunities when it comes to finding a job; we communicate over networks, be they online or offline; etc.
In this course we aim to produce a detailed understanding of the web of social contacts that structure our daily life and society. We will consider the network both as an object that is interesting in its own right and as something that creates co-dependencies between social units in terms of outcomes and properties of these social units themselves. The overarching goal of the course is to provide us with tools that bridge theories on the one hand, and what we can actually observe in observational and archival empirics on the other. Put another way, we aim to avail ourselves of approaches that permits us to test if our theoretical ideas about social interaction are supported by what people, organisations and countries actually do.
The course is structured around a collection of themes based on such theoretical concepts such as cohesion, embeddedness, homophily, transitivity, the Mathew effect, structural holes, influence, selection. We will examine these both from the perspective of how they structure the network and how these network effects structure behaviour, opinions and beliefs.
For the purposes of getting some practical understanding of the approaches presented, we will also explore analytic methods using block models, stochastic actor-oriented models, exponential random graph models, network autocorrelation and network effects models. It is not expected that the students become expert users in any of these methods but to appreciate the common goal across these models, namely to model and take into account the interdependencies.
Data will mostly be handled in R but orientation to other analysis packets will be given. Although not a strict prerequisite, you are strongly encouraged to have taken SOCY20042 in year 2. Other quantitative courses such as ’Survey Design and Analysis for Social Scientists’ and ’Modelling Social Inequality’ or something equivalent may prove very helpful as well.
The unit aims to:
(i) Introduce a toolbox for empirical investigation of theories on social interaction and complexity.
(ii) Introduce the practical issues involved in managing and analysing network data.
(iii) Provide a theory and research driven perspective on everyday observables while also providing the students with the skills, confidence and knowledge to solve analytical puzzles in a wide array of applied contexts, from organisations to the spread of infectious diseases.
(iv) Give the students a working handle on basic analysis tools.
(v) Foster a familiarity with an extensive list of more advanced analysis tools and methods at a level that enables the student to further their skills in relevant areas.
(v) Provide the analytical framework for critically appraisal of quantitative statements in social networks and related areas.
Student should/will (please delete as appropriate) be able to:
Knowledge and Understanding: An understanding of the empirical requirements and evidence needed for drawing conclusions about complex social processes. A broad knowledge of fundamental concepts in social network analysis, both theoretical and technical.
Intellectual skills: Relate concepts such as micro-macro, self organisation and emergence to specific predictions and hypothesis for observables. Be able to choose approapriate approach for a particular set of research questions. A detailed appreciation of the appropriateness of methods used in studies and a keen, critical eye to potential sources of error.
Practical skills: Skills in using social network datasets and practical experience of data analysis including using software (sna, network, pnet, RSiena). Visualising, describing, and reporting results for social network analysis and drawing conclusions about social processes. Basic skills in using and applying essential mathematical concepts such as density and clustering coefficients.
Transferable skills and personal qualities: Data handling, interpretation and reporting of quantitative analysis. The course provides a toolbox whose acquired skills will make the student a network analyst with unique skills on the labour market. The well trained network analyst is a scarce resource and is uniquely equipped to answer questions in a wide array of areas, from organisational problems in business to disease spread in populations as well as being able to provide unique solutions to address concerns about information flow from a government or business perspective.
Teaching and learning methods
Please note the information in scheduled activity hours are for guidance only and may change.
The course will involve: lectures, group work and computing lab classes. Extensive use will be made of relevant on-line resources where students can learn about social network data.
Blackboard resources will be used to enable students to access teaching data and data sources.
The lecture component will provide a theoretical and methodological framework for learning about the analysis of social network data and the key pathways from theory to subjecting research questions to empirical scrutiny. Group work in the tutorials will give students an opportunity to pool their different resources and competencies and learn from each other. Practical sessions in the computing lab will give students hands on experience in the basic types of analysis as well as interpretation and reporting results. Such skills are highly transferable.
The emphasis on the use of real data to answer research questions that are mediated through real, everyday questions is designed to engage students and enable students to consider using such approaches as part of their own dissertation research. Many of the theoretically grounded theories are of relevance for businesses and corporations and the student will invest interest as a result of the practical relevance of the material presented.
The course is seen as having the specific global aim of furthering the quantitative numeracy of social scientist at Manchester, and by extension, in the UK. In combination with other UG courses offered by SSDA, students will be encourage and enthused by the prospect of furthering their education in quantitative social science on the PG level.
|Written assignment (inc essay)||100%|
All Social Statistics courses include both formative feedback - which lets you know how you’re getting on and what you could do to improve - and summative feedback - which gives you a mark for your assessed work.
Borgatti, S.P., Everett, M.G., Johnson, J.C. (2013). Analyzing Social Networks. Sage.
Fernandez, Roberto M., and Roger V. Gould. 1994. ’A Dilemma of State Power: Brokerage and Influence in the National Health Policy Domain.’ The American Journal of Sociology 99(6):1455-1491.
Granovetter, Mark. 1973. ’The Strength of Weak Ties.’ American Journal of Sociology 78(6):1360-80.
Hanneman, R.A. and Riddle. M. (2005) Introduction to social network methods. Riverside, CA: University of California, Riverside
Lusher, D., Koskinen, J., Robins, G., (2013). Exponential Random Graph Models for Social Networks: Theory, Methods and Applications, New York: Cambridge University Press.
Moody, James, and Douglas R. White. 2003. ’Structural Cohesion and Embeddedness: A Hierarchical Concept of Social Groups.’ American Sociological Review 68(1):103-127.
Scott, J., Carrington, P. (2011) Handbook of Social Network Analysis. Sage Publications, London.
Mitchell Centre www.ccsr.ac.uk/mitchell
|Scheduled activity hours|
|Practical classes & workshops||10|
|Independent study hours|
|Termeh Shafie||Unit coordinator|