SOS9227 – Social Network Analysis
Course content
This one-week course offers a hands-on introduction to the concepts and methods of social network analysis (SNA) using the statistical program R. Depending on research interests and data availability, researchers commonly look at networks of different sizes and degrees of complexity. In this class we will learn about tools to analyze these various types of networks. This will include egocentric networks (e.g. my own friends and family) and complete networks of various sizes: e.g. friendship ties in a school class, communication networks of an organization, and large-scale, complex networks like co-citation networks of scientists or interaction networks on social networking websites. The course will combine lecture/seminar style presentations of the instructor with lab sessions in which students work on empirical exercises using R. In addition to R, we will use the open source program Visone for the visualization of networks. Visone works in combination with R.
Learning outcome
The course will cover a wide range of topics including the following:
- Introduction to R and Visone
- Basic terminology of social network analysis
- SNA data management (adjacency matrices, edge lists, etc.)
- Data sources for SNA
- Actor-based measures (e.g. degree, centrality measures)
- Network-based measures (e.g. centralization, degree of clustering, average path length)
- Methods for detecting network clusters and communities
- Bipartite networks
- Complex network models (e.g. small-world and scalefree networks)
- Network visualization
- Exponential random graph models (ERGMs) / Siena
Admission
PhD students at the University of Oslo register for the course in StudentWeb.
Others apply through this application form.
Registration deadline is 7th May 2014.
Prerequisites
Recommended previous knowledge
The course is for anyone who wants to understand and apply basic social network analysis. It is intended for graduate students in the social sciences. Although not required, it is recommended that students have some prior experience with statistical software on the level of writing syntax.
Teaching
Example data will be used in the analyses. Participants may benefit from analyzing their own data during the lab sessions.
The course will be held in PC-lab 035, Harriet Holters Building (Moltke Moes Street 31, Blindern, Oslo) all days.
Preliminary schedule
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Time |
4th June |
5th June |
6th June |
10th June |
11th June |
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Introduction to R |
Introduction to SNA |
Small-scale networks |
Complex Networks |
Advanced Topics |
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10:00 |
Overview of class contents |
Introduction to SNA |
Theoretical concepts & measures |
Theoretical concepts & measures |
Exponential random graph models (LAB) |
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11:00 |
Basic intro-duction to R |
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12:00 |
Lunch |
Lunch |
Lunch |
Lunch |
Lunch |
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13:00 |
Further topics in R (LAB) |
Importing and working with network data in R (LAB) |
Analyzing small-scale networks in R (LAB) |
Analyzing complex networks in R (LAB) |
Brief intro: R Siena (LAB) |
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14:00 |
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15:00 |
Discussion of topics for student papers |
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16:00 |
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17:00 |
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Please note that details in this schedule may change.
Recommended introductory readings:
SNA
Borgatti, Stephen P., Ajay Mehra, Daniel J. Brass, and Giuseppe Labianca. 2009. “Network Analysis in the Social Sciences.” Science 323(5916):892–95.
Scott, John P. 2000. Social Network Analysis: A Handbook. 2nd ed. London: Sage.
Jackson, Matthew O. 2008. Social and Economic Networks. Princeton: Princeton University Press.
Scott, John. 1988. “Social Network Analysis.” Sociology 22(1):109–27.
R
Muenchen, Robert A., and Joseph M. Hilbe. 2010. R for Stata Users. New York: Springer.
Zuur, Alain F., Elena N. Ieno, and Erik H. W. G. Meesters. 2009. A beginner’s guide to R. Dordrecht: Springer.
(These are just recommendations. Other introductory texts to SNA and R will be fine, too.)
Instructor:
Sebastian Schnettler, Department of Sociology, University of Konstanz. E-mail: sebastian.schnettler@uni-konstanz.de
Examination
Participants obtain 6 ECTS credits by completing the course requirements, which are active participation in the course and submission of a paper.
Deadline for submitting the paper is not decided yet.
Language of examination
The examination text is given in English, and you submit your response in English.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.