Boston College, Massachusetts USA.

e-mail: borgatts@bc.edu

Martin Everett

University of Westminster, United Kingdom.

e-mail: M.Everett01@westminster.ac.uk

Wednesday, February 16

9:00am - 5:00pm

Cost: Students $50, all others $100

A beginners tutorial on the concepts,
methods and data analysis techniques of social network analysis. The
course begins with a general introduction to the distinct goals and
perspectives of network analysis, followed by a practical
discussion of network data, covering issues of collection, validity,
visualization, and mathematical/computer representation. We then take
up the methods of detection and description of structural
properties such as centrality, cohesion, subgroups, cores, roles,
etc. Finally, we consider how to frame and test network hypotheses. An
important element of this workshop is that all participants are given a
demonstration version of UCINET 6 for Windows and the Netmap
visualization software, which we use to provide hands-on experience
analyzing real data using the techniques covered in the workshop. In
order to participate fully in the workshop, participants should bring
laptop computers so that they can run the analyses on their machines at
the same time as they are being demonstrated by the instructors.

ICS, University of Groningen. The Netherlands

e-mail: t.a.b.snijders@ppsw.rug.nl

Wednesday, February 16

10:00am - 12:30pm and 2:00pm - 4:30pm

Cost: $50, students $25

Longitudinal social network data are
understood in this workshop as two or more repeated observations of a
directed graph on a given node set (usually between 30 and 100 nodes,
sometimes up to a few hundreds). In other words, this workshop is about
statistical modeling of the dynamics of complete networks. The workshop
teaches the statistical method to analyze such data, as described in
Sociological Methodology - 2001, p. 361-395, and implemented in the
SIENA program. The statistical model used for the network evolution
allows various network effects (reciprocity, transitivity, cycles,
popularity, etc.), effects of individual covariates (covariates
connected to the sender, the receiver, or the similarity between sender
and receiver), and of dyadic covariates. One interpretation of this
model is an actor-oriented model where the nodes are actors whose
choices determine the network evolution. Further information about this
method, including references and a JAVA demo, can be found at website
http://stat.gamma.rug.nl/snijders/siena.html. The statistical analysis
is based on Monte Carlo simulations of the network evolution model and
therefore is a bit time-consuming. The computer program SIENA is
included in the package StOCNET which runs under Windows. The workshop
will demonstrate the basics of using StOCNET and SIENA. Attention will
be paid to the underlying statistical methodology, to examples, and to
the use of the software. The morning session will focus on the
intuitive understanding of the model and operation of the software. The
afternoon will continue this, and also present some further
mathematical background. Special attention will be paid to a recent
development: models for the simultaneous dynamics of networks and
behavior. Participants are requested to check website
http://stat.gamma.rug.nl/snijders/siena.html in the week before the
workshop to download the workshop materials.

University of Ljubljana.

e-mail: vladimir.batagelj@uni-lj.si

Andrej Mrvar

University of Ljubljana.

e-mail: andrej.mrvar@uni-lj.si

Wouter de Nooy

Erasmus University Rotterdam.

e-mail: denooy@fhk.eur.nl

Wednesday, February 16

9:00am - 5:00pm

Cost: $50, students $25

The workshop consists of three parts.
In the first part we will give an introduction to the use of Pajek
based on our textbook on social network analysis 'Exploratory Social
Network Analysis with Pajek'. In the second part we will explain how to
use multi-relational networks (new in Pajek 1.02, November 2004) and
present some efficient approaches (valued cores, triangular and short
cycle connectivity, citation weights, pattern search, generalized
blockmodeling, islands) to analysis and visualization of real-life large
network (genealogies, collaboration networks, citation networks, Internet
networks, dictionary networks, 2-mode networks). We will also discuss the
'fine-tuning' of Pajek's layouts (pictures) and combining Pajek with
statistical program R.

In the last part participants will have an opportunity to discuss about the use of Pajek (questions, suggestions, analysis of specific data...). Jurgen Pfeffer, from FAS.research, Vienna will present his program Text2Pajek that converts excel/text file datasets into Pajek format.

To actively follow the workshop bring your laptop with you. Program Pajek is available at http://vlado.fmf.uni-lj.si/pub/networks/pajek/.

In the last part participants will have an opportunity to discuss about the use of Pajek (questions, suggestions, analysis of specific data...). Jurgen Pfeffer, from FAS.research, Vienna will present his program Text2Pajek that converts excel/text file datasets into Pajek format.

To actively follow the workshop bring your laptop with you. Program Pajek is available at http://vlado.fmf.uni-lj.si/pub/networks/pajek/.

University of Toronto, Canada

e-mail: wellman@chass.utoronto.ca

Wednesday, February 16

2:00pm - 5:00pm

Cost: $35

This is a non-technical introduction to
social network analysis. It
describes the development for social network analysis, some key
concepts, and some key substantive methods and findings. It is aimed at
newcomers to the field, and those who have only seen social network
analysis as a method.

Simon Fraser University, Canada

email: seary@sfu.ca

Bill Richards

Simon Fraser University, Canada

email: richards@sfu.ca

Wednesday, February 16

9:00am - 12:00

Cost: $40 (includes a MultiNet CD and printed manual)

MultiNet is an interactive computer
program for the analysis and display of discrete and continuous network
data. It simultaneously examines characteristics of links and nodes.
The program is menu-driven, it has context-sensitive, interactive,
on-line help, and always presents a color graphic representation of the
data or the results of analysis as well as a textual report. The
program does univariate descriptive statistics, crosstabulation,
analysis of variance, regression, correlation, p*, and eigen analysis.
It has powerful and flexible data manipulation capabilities. It
performs continuous and discrete transformations, such as ordination,
quantiles, recategorization; linear, log, power, and z transforms. New
variables can be created by transforming or combining existing ones in
any manner describable by algebraic equations. The program also
provides file viewing and editing.

Part 1. Managing complex data

Part 1. Managing complex data

MultiNet is a program designed for
exploring many types of relationships in complex network data. We
discuss the univariate and multivariate methods currently available for
exploring both attribute (node) and network (link) variables. These
include discrete and continuous data recoding and bivariate and
trivariate methods applied to node and link variables by themselves, as
well as within networks. These methods will be demonstrated on real
network data.

Part
2. Spectral Analysis

MultiNet does four types of eigen
decomposition for spectral analysis of networks with up to 5,000 nodes
with interactive graphical display of results in 1, 2, or 3 dimensions.
We will demonstrate the analytic procedure; explain the various options
available for interactive display of results; and show how the results
from this procedure are integrated with the rest of the program and how
both coordinates in eigen space and partitions can be used as variables
in any other subsequent analysis.

Part
3. Hybrid methods

We describe hybrid methods which allow
creating node variables from networks, such as eigenvectors,
partitions, and various centrality measures. We also describe methods
for creating link variables from node attributes, and groupings of link
variables. These methods will be demonstrated on real network data.

Part
4. p* in MultiNet

We describe the implementation of p* in
MultiNet, and discuss various aspects of p* fitting with special types
of data: large; symmetric; bipartite; multiple network. Since the
current version can handle up to 5,000 nodes and 256 parameters,
managing the displays and reports can be quite complex. We demonstrate
how this implementation may be applied to some moderately large
datasets.

Part
5. MultiNet in action

We apply topics covered in the
proceeding parts to analyse moderately large, complex datasets from
medicine. Topics applied include eigenspaces, hybrid data creation and
recoding, bipartite p* fitting, and network crosstabs.