This course is discontinued

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

Time

4th June

5th June

6th June

10th June

11th June

 

Introduction to R

Introduction to SNA

Small-scale networks

Complex Networks

Advanced Topics

 

 

 

 

 

 

10:00

Overview of class contents

Introduction  to SNA

Theoretical concepts & measures

Theoretical concepts & measures

Exponential random graph models (LAB)

11:00

Basic intro-duction to R

12:00

Lunch

Lunch

Lunch

Lunch

Lunch

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)

14:00

15:00

Discussion of topics for student papers

16:00

17:00

 

 

 

 

 

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.

Facts about this course

Credits

6

Level

PhD

Teaching

Spring 2014

4-6th June and 10-11th June 2014

Examination

Spring 2014

Teaching language

English