Social Network Analysis, TT 2012.
Course providers:
Overview
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.
Objectives
Upon completion of the course the students should:
 be able to formulate meaningful research questions concerning social network analysis;
 have ideas about how to gather relevant network data, and some of the associated questions and problems;
 have access to a variety of descriptive measures for networks and software to calculate them, and have the ability to interpret the results;
 be able to formulate agentbased models which from local behaviour of agents can generate networks with diverse global structures;
 know how to conduct computer simulations to analyze properties of such models;
 be able to specify stochastic actororiented models for analyzing dynamics of panel observations of networks, and of networks and individual behaviour;
 know how to execute and interpret analysis of an empirical longitudinal network data set by stochastic actororiented models.
Meeting Schedule and handouts
Location:
Manor Road Building.
Time schedule for TT 2012.
(Teachers indicated by FRS and TS.)
Week 1:
 Thursday, April 26, 2.004.00 pm, FRT, seminar room B.
Graphs and matrix representations.
 Friday, April 27, 2.004.00 pm, IT room.
Introduction to R by Dr Tak Wing Chan.
(important unless you already have a good working knowledge of R).
Week 2
 Thursday, May 3, 2.004.00 pm, TS, seminar room B.
 Handout.
 Example 1:
F.R. Pitts (1979),
The medieval trade network of Russia revisited.
Social Networks, 1, 285292.
 Example 2:
Marc Flandreau and Clemens Jobst,
"The Ties that Divide: A Network Analysis of the International Monetary System, 18901910",
The Journal of Economic History, 65 (2005), 9771007.
 Example 3:
Peter S. Bearman, James Moody, and Kate Stovel (2004),
Chains of Affection: The Structure of Adolescent Romantic and Sexual Networks.
The American Journal of Sociology, 110, 4491.
 Structural equivalence, regular equivalence. Block modeling.
Handout:
Equivalences and blockmodeling.
 Anuška Ferligoj, Vladimir Batagelj, Andrej Mrvar,
Course on blockmodeling, see pages 816.
 Another example, not treated:
John F. Padgett and Christopher K. Ansell (1993),
Robust Action and the Rise of the Medici, 14001434.
The American Journal of Sociology, 98, 12591319.
Thursday, May 3, 4.006.00 pm, TS, IT room (computer class).
Week 3
 Thursday, May 10, 2.004.00 pm, FRT, seminar room B.
Linking local and global properties.
Week 4
 Tuesday, May 15, 5.007.00 pm, TS, seminar room A.
Wednesday, May 16, 5.007.00 pm, TS, seminar room B.
Topic to be treated:
 Local structure in social networks.
Literature for this topic:
 Statistical analysis of network dynamics.
Network panel data.
Stochastic actororiented models for network dynamics.
Literature for this topic:
Week 5
 Thursday, May 24, 2.004.00 pm, FRT, seminar room B.
Generative Models and Network Growth.
Week 6
 Tuesday, May 29, 9.0011.00 am, TS, seminar room B.
Topic to be treated:
 Studies of networks and behaviour.
Selection and influence.
Literature for this topic:
Thursday, May 31, 9.0011.00 am, TS, IT room (computer class).
 See the SIENA website.

Ripley, R., and Snijders, T.A.B. (2012)
Manual for SIENA version 4.0. University of Oxford: Department of Statistics and Nuffield College.
 Rscript01DataFormat.R
with some basic information about R, networks, data formats etc;
with an example data file arclistdata.dat.
 s50 data set, a 50actor excerpt from the Teenage Friends and Lifestyle Study data set
of West et al.:
description and
data set (longitudinal, 3 waves, networks and behaviour);

Rscript02SienaVariableFormat.R
for how to specify data as variables in RSiena, and specify the model;

Rscript03SienaRunModel.R
for how to carry out the estimation and look at the results;

Rscript04SienaBehaviour.R
for how to specify models for dynamics of networks and behaviour;
 van de Bunt students data description and
data set (longitudinal)
 description and
data set
of the data (longitudinal, networks and behavior)
collected by Andrea Knecht.
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.
Assessment
The students will be assessed by means of an essay of at most 5000 words (advised length 30004000 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 10002500 words. The assignments
will be returned, with comments from the instructors, in week 6 or early week 7.
General background literature

 Borgatti, S.P. and Kidwell, V. In Press. "Network Theorizing". In Carrington, P. and Scott, J. (eds) The Sage Handbook of Social Network Analysis. Sage Publications
 Stephen P. Borgatti, Ajay Mehra, Daniel J. Brass, Giuseppe Labianca,
Network Analysis in the Social Sciences,
Science 323, 892895 (2009).

Introduction to social network methods (2005),
a free online introductory textbook on social network analysis by
Robert Hanneman and Mark Riddle.
There is also a
pdf version of this text.

Chris Caldwell: Graph theory Glossary at
http://www.utm.edu/departments/math/graph/glossary.html

Hanneman, R. Introduction to Social Networks.
Online book free on the web at
http://faculty.ucr.edu/~hanneman/nettext/

Marsden, P.V. (1990) Network data and measurement. Annual Review of Sociology 16:43563.
 Marsden, P.V. (2005) Recent developments in network measurement. Chapter 2 (pp. 830) in
P.J. Carrington, J. Scott, and S. Wasserman (eds.), Models and methods in social network analysis, (2005). Cambridge University Press.

Moody, J. and Paxton, P. (2009), Building Bridges : Linking Social Capital and Social Networks to Improve theory and research . American Behavioral Scientist, 52, 14911506.



Newman, M.E.J. (2010) Networks: An Introduction , Oxford University Press, Oxford.
This is introduced as an alternative general reference which covers many of the topics discussed, although not only from the perspective of social network analysis.
 Travers, J. and Milgram, S. (1969) An experimental study of the small world problem. Sociometry 32:425443.
 Dodds, P. S., Muhamad, R., and Watts, D.J. (2003) An experimental study of search in global social networks. Science 301:827829.

Watts, D.J. and Strogatz, S.H. (1998) Collective dynamics of "smallworld" networks. Nature 393: 440442.
For an implementation of the WattsStrogatz model in R, look at watts.strogatz.game in igraph.
 Kleinberg, J.M. (2000) Navigation in a small world. Science 406:845.
 Watts, D.J. (2004) The "new" science of networks. Annual Review of Sociology 30:243270.
 Fleming, L., King III, C. and Watts, D.J. (2007) Small worlds and regional innovation. Organization Science 18:938954.
 Centola, D. (2010) The spread of behaviour in an online social networking experiment. Science 329:11941197.

 Clauset, A., Shalizi, C.R., and Newman, M.E.J. (2009) Powerlaw distributions in empirical data. SIAM Review 51:661703.
To implement the analysis described in this paper, you can obtain the code in R from http://tuvalu.santafe.edu/~aaronc/powerlaws/.
 Barabási, A.L. and Albert, R. (1999) Emergence of scaling in random networks. Science 286: 509512.
For an implementation of the BarabasiAlbert model in R, look at barabasi.game in igraph.
 Merton, R. (1968) The Matthew effect in science. Science 159:5663.
 Amaral, L.A.N., Scala, A., Barthelemy, M. and Stanley, H.E. (2000) Classes of small world networks. Proceedings of the National Academy of Sciences 97:1114911152.
 Powell, W.W., White, D.R., Koput, K.W. and OwenSmith J (2005) Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences. American Journal of Sociology 69:213238.
 Saavedra, S., ReedTsochas, F. and Uzzi, B. (2008) Asymmetric disassembly and robustness in declining networks. Proceedings of the National Academy of Sciences 105:1646616471.
 Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 19631999. American Sociological Review
69:213238.
 Guimerà, R., Uzzi, B., Spiro, J., and Amaral, L.A.N. (2005) Team assembly mechanisms determine collaboration network structure and team performance. Science 308:697702.
 Uzzi, B. and Spiro, J. (2005) Collaboration and creativity: The small world problem. American Journal of Sociology 111:447504.
 Robins, G.L., Woolcock, J., and Pattison, P. (2005)
Small and other worlds: Global network structures from local processes.
American Journal of Sociology , 110, 894936.


Snijders, T.A.B., Steglich, C.E.G., and van de Bunt, G.G., (2010).
Introduction to actorbased models for network dynamics. Social Networks, 32, 4460.
Illustrative applications:

Maarten Van Zalk / Selfhout, William Burk, Susan Branje, Jaap Denissen, Marcel van Aken, and Wim Meeus (2010).
Emerging late adolescent friendship
networks and big five personality traits: A social network approach. Journal of Personality, 78, 509538.

Noona Kiuru, William J. Burk, Brett Laursen, Katariina SalmelaAro, and JariErik Nurmi (2010).
Pressure to drink but not to smoke: Disentangling
selection and socialization in adolescent peer networks and peer groups. Journal of Adolescence, 33, 801812.

Ripley, R., and Snijders, T.A.B. (2011)
Manual for SIENA version 4.0. University of Oxford: Department of Statistics and Nuffield College.

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

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".

Excellent simple intro to R from Princeton.

Getting started with R by J. Gardner.
 The official R intro

A portal with pointers to other R resources .
 R Starter kit from UCLA.
 Quick R website
(intended especially for experienced users of other statistical software).
Materials introducing the package RSiena.
 See the SIENA website.

Ripley, R., and Snijders, T.A.B. (2012)
Manual for SIENA version 4.0. University of Oxford: Department of Statistics and Nuffield College.
 Rscript01DataFormat.R
with some basic information about R, networks, data formats etc;
with an example data file arclistdata.dat.
 s50 data set, a 50actor excerpt from the Teenage Friends and Lifestyle Study data set
of West et al.:
description and
data set (longitudinal, 3 waves, networks and behaviour);

Rscript02SienaVariableFormat.R
for how to specify data as variables in RSiena, and specify the model;

Rscript03SienaRunModel.R
for how to carry out the estimation and look at the results;

Rscript04SienaBehaviour.R
for how to specify models for dynamics of networks and behaviour;
 van de Bunt students data description and
data set (longitudinal)

Assignment about homophily and transitivity,
using one of the school classes of Andrea Knecht;

RscriptASNAassignement.R
Script for the assignment.
 description and
data set
of the data (longitudinal, networks and behavior)
collected by Andrea Knecht.