Training and Events
Social Network Analysis
|London School of Economics and Political Science|
Professor Peter Bearman
02/05/2017 - 05/05/2017
4th Floor, Lionel Robbins Building, Portugal Street, London
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Esti Sidley, 0207 955 6947, email@example.com
Tuesday-Friday, 2-5 May 2017, 10:00 - 12:00 and 14:00 - 16:00
This four-day short course is taught by Professor Peter Bearman (Columbia University, and LSE Centennial Professor at the Departments of Methodology and Sociology).
Social Network Analysis introduces conceptual tools that help us better understand social structure and social action; and provides and introduction to a set of methods and approaches in social network analysis. The course focuses on both long-standing traditions within network thinking and new ways to visualize and analyze network data. Networks are, at their core, actors tied through relations and in this course, actors and relations will be defined in many different ways depending on the puzzles at hand, for example, they could be families tied to one another though marriage, firms engaging in exchange, or organizations linked through career mobility of individuals. The theoretical concepts will be implemented using the statistical software R. No previous programming experience is required: familiarity with R and the relevant packages will be built up through the course.
We start with the theoretical foundations of structural analysis, including the parsimonious ways of describing social structures (i.e., density, degree distribution and centrality) and the principles to account for change in structures over time (i.e., balance, homophily and transitivity). We build on these basics to then turn to modularity: the “groupiness” of structures and talk about cohesion and the relationship between categories and networks. We will introduce more nuanced concepts, such as structural equivalence, brokerage, and multiplexity of ties. Finally, we will delve into methods to compare network structures, think about diffusion dynamics, and large-scale structural change.
Students will be encouraged to read along with the material presented in the class but such reading is not required. Articles associated with each topic will be made available for those with interest in one or another specific measure, substantive area, or method.
Students will learn how to work with network data and one of the first exercises will involve our generating class-based network data as an example data structure, which we will all use. Students will need to bring their own laptops to the class.
Note: The session on Tuesday is an introduction to the R software, to the extent that is necessary to follow the computer classes of the course. You can skip these if you are familiar with R and the RStudio interface. The main course is on Wednesday-Friday.
Intermediate (some prior knowledge)
Website and registration
R, social network analysis
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