Social Network Analysis, TT 2012.

Course providers:


This course is an introduction to social network analysis, with a focus on modelling. The aim is to give students an overview of research questions connected to social networks, and of descriptive measures, models, and methods of analysis that can be used to analyze empirical social network data.


Upon completion of the course the students should:

Meeting Schedule and handouts

Location: Manor Road Building.

Time schedule for TT 2012.
(Teachers indicated by FRS and TS.)

    Week 1:
  1. Thursday, April 26, 2.00-4.00 pm, FRT, seminar room B.
    Graphs and matrix representations.
  2. Friday, April 27, 2.00-4.00 pm, IT room.
    Introduction to R by Dr Tak Wing Chan.
    (important unless you already have a good working knowledge of R).

    Week 2

  3. Thursday, May 3, 2.00-4.00 pm, TS, seminar room B.
    Thursday, May 3, 4.00-6.00 pm, TS, IT room (computer class).

    Week 3

  4. Thursday, May 10, 2.00-4.00 pm, FRT, seminar room B.
    Linking local and global properties.

    Week 4

  5. Tuesday, May 15, 5.00-7.00 pm, TS, seminar room A.
    Wednesday, May 16, 5.00-7.00 pm, TS, seminar room B.
  6. Topic to be treated:

    Week 5

  7. Thursday, May 24, 2.00-4.00 pm, FRT, seminar room B.
    Generative Models and Network Growth.

    Week 6

  8. Tuesday, May 29, 9.00-11.00 am, TS, seminar room B.

    Topic to be treated:

    Thursday, May 31, 9.00-11.00 am, TS, IT room (computer class).

Computer software

The course will use appropriate packages of R. This will include the packages sna, igraph, and RSiena. There will be a crash course in R in week 1 of Trinity Term, offered by the Department of Sociology.


The students will be assessed by means of an essay of at most 5000 words (advised length 3000-4000 words) which must be submitted before the end of week 9 of Trinity Term. The essay will have to contain an analysis of observed or simulated network data. The topic should be agreed upon by the student and one of the course instructors before or within week 6.

Practice assignment

An assignment will be given to the students in week 4, to be completed before the end of week 6. This will be an analysis of an observed network data set or the exploration of a network simulation model.

The instructors will provide a variety of questions concerning data sets and simulation models, so that for each different question for a data set or model, there will be no more than two students working on it.

This assignment will be for feedback purposes only, and students are allowed to collaborate. The assignment should be fulfilled by an essay of 1000-2500 words. The assignments will be returned, with comments from the instructors, in week 6 or early week 7.

General background literature

R materials


Some websites and resources that can be very helpful for the beginning R user:

  1. Some Hints for the R Beginner by Patrick Burns with the memorable quote "I asked R users what their biggest stumbling blocks were in learning R. A common answer that I was quite surprised by was that the biggest stumbling block was thinking that R was hard".
  2. Excellent simple intro to R from Princeton.
  3. Getting started with R by J. Gardner.
  4. The official R intro
  5. A portal with pointers to other R resources .
  6. R Starter kit from UCLA.
  7. Quick R website (intended especially for experienced users of other statistical software).

Materials introducing the package RSiena.