Statistical Models for Social Networks

SOST71032 Statistical Models for Social Networks 15 Semester 2 Postgraduate Taught Level 7 Nikita Basov Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 The course uses lectures and computer lab practicals to introduce the motivation, theory, and application of statistical models for social network analysis. Students learn posing network-oriented research questions and picking appropriate data and methods to answer them, managing network data, visualizing networks, calculating key network measures, modelling networks, and interpreting the results.  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.   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 studiesD3. 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.  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. 0 Other 100% 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 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.   Y 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:  <a href="http://www.library.manchester.ac.uk/academicsupport/informationandadviceonlink2listsreadinglistsoftware/" rel="noreferrer noopener" target="_blank">http://www.library.manchester.ac.</a>  <a href="http://www.library.manchester.ac.uk/academicsupport/informationandadviceonlink2listsreadinglistsoftware/" rel="noreferrer noopener" target="_blank">uk/academicsupport/informationandadviceonlink2listsreadinglistsoftware/</a>   0 0 120 Scheduled activity hours 30 hours (mixed lecture/tutorial format)