Methods for Social Network Analysis, TT 2009.
Course provider: Tom A.B. Snijders
(tom.snijders "at" nuffield.ox.ac.uk;
http://stat.gamma.rug.nl/snijders).
This course is part of the
methodology courses offered at the Department of Politics and International Relations
of the University of Oxford, and also available to students from other departments.
The course is part of the teaching within the
Centre for Research Methods in the Social Sciences.
Overview
Social networks are structures of relations between social actors
- e.g., friendship between individuals, collaboration between companies,
trade between countries, etc.
They are important both in themselves and for explaining
a variety of actor-dependent characteristics
(behavioural tendencies, attitudes, performance, etc.)
Particularly interesting is the two-way influence
between relational networks and individual actions and outcomes.
The data structure of social networks, where the usual
statistical assumption of independent cases is totally implausible,
poses special requirements for data analysis.
This course presents a number of important data analysis methods
for social networks.
The material presented at this website is too extensive for the course.
Only a selection of this will be treated.
The course is about five main topics:
- Centrality and other positional characteristics of actors.
- Concepts of positional equivalence.
- The exponential random graph model (ERGM),
a statistical model for single observations of networks.
- Stochastic actor-based models for network dynamics
(i.e., for analysing longitudinal network data).
- Stochastic actor-based models for the
simultaneous dynamics of networks and
behaviour (which here is a term referring generally to
changeable actor characteristics such as behavioural tendencies
or performance).
The focus is on the last three topics.
They are treated in the framework of statistical
inference - which is a usual framework for
most data analysis but less so for social network analysis.
The course includes computer classes for hands-on data analysis
using the
StOCNET and
SIENA computer programs.
From these websites you can download the programs.
You can also download the
StOCNET manual and the
the most recent version
of the Siena 3.2 manual.
The course takes place in Trinity Term 2009 in weeks 2 and 4 (see below for meeting schedule).
All papers to which links are included in this page,
are intended only for personal use by participants in this course.
Background: introductory literature
It should be noted that this course is not a general introduction
to Social Network Analysis,
but a specific introduction to statistical methods for
Social Network Analysis.
For a general introduction, have a look at
Meeting Schedule
Location:
Manor Road Building.
Time schedule for TT 2009.
- Monday, May 4, 12.00-2.00, seminar room E.
- Tuesday, May 5, 9.00-10.00, IT room (computer class).
- Wednesday, May 6, 9.00-11.00, seminar room B.
- Monday, May 18, 12.00-2.00, seminar room E.
- Tuesday, May 19, 9.00-10.00, IT room (computer class).
- Wednesday, May 20, 9.00-11.00, seminar room B.
Lecture Notes
For printing the handouts from Acrobat reader,
if it does not come out righ,
choose the scaling
option "Fit to printer margins".
- Centrality and positional characteristics.
- Positional equivalence.
- The exponential random graph model (ERGM).
- Stochastic actor-based models for network dynamics.
- Stochastic actor-based models for the
simultaneous dynamics of networks and behaviour.
Reading list
- General introduction to Social Network Analysis (for browsing only):
- For the ERG model:
-
Robins, G., Pattison, P., Kalish, Y., and Lusher, D. (2007).
An introduction to exponential random graph (p*) models for social
networks. Social Networks, 29, 173-191.
-
Robins, G., Snijders, T., Wang, P., Handcock, M., and Pattison,
P. (2007).
Recent developments in Exponential Random Graph (p*)
Models for Social Networks. Social Networks, 29, 192-215.
-
For longitudinal models:
-
Snijders, T.A.B., van de Bunt, G.G., and Steglich, C.E.G., (2009).
Introduction to actor-based models for network dynamics.
Social Networks, in press.
-
Steglich, C.E.G., Snijders, T.A.B. and Pearson, M. (2007).
Dynamic Networks and Behavior: Separating Selection from Influence.
Submitted for publication.
Background literature
-
For the ERG model:
- Snijders, Tom A.B., Pattison, Philippa E., Robins, Garry L., and Handcock, Mark S.,
New specifications for exponential random graph models.
Sociological Methodology, 2006, 99-153.
-
For longitudinal models:
- Snijders, T.A.B. 2005.
Models for Longitudinal Network Data.
Chapter 11 in P. Carrington, J. Scott, and S. Wasserman (Eds.),
Models and methods in social network analysis.
New York: Cambridge University Press.
- Example for network dynamics:
van de Bunt, G.G., Van Duijn, M.A.J., and Snijders, T.A.B.,
Friendship networks through time:
An actor-oriented statistical network model.
Computational and Mathematical Organization Theory,
5 (1999), 167-192.
- Example for dynamics of networks and behaviour:
William J. Burk, Margaret Kerr, and Håkan Stattin (2008),
The co-evolution of early adolescent friendship networks, school involvement,
and delinquent behaviors.
Revue Française de Sociologie, 49, 499-522.
- General background literature for Social Network Analysis:
-
Stanley Wasserman and Katherine Faust ,
Social Network Analysis: Methods and Applications.
Cambridge University Press, 1994.
- Peter Carrington, John Scott, Stanley Wasserman (eds.),
Models and Methods in Social Network Analysis.
Cambridge University Press, 2005.
- Alain Degenne and Michel Forse,
Introducing Social Networks. Sage, 1999.
- John Scott,
Social Network Analysis: A Handbook. 2nd edition.
Sage, 2000.
- Wouter de Nooy, Andrej Mrvar, and Vladimir Batagelj,
Exploratory Social Network Analysis with Pajek.
Cambridge University Press, 2005.
- General introductory and reference material for Social Network Analysis: