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Hi Daniele,

MultiNet started as an extended replacement for FATCAT, but it does so 
much more that there is little in common beyond the data model it uses. 
MultiNet's immediate ancestor was designed specifically to work with 
ego-centric data. MultiNet goes much further than its predecessor in 
that it is happy with continuous data as well as the categorical data 
that FATCAT wanted. It also does many more types of analysis than the 
earlier program, and it can use ordinary (non-ego-centric) data. Largely 
due to the mix of analytic techniques it incorporates, .MultiNet can 
extract information about patterns in the data that are otherwise not 
easily seen.

MultiNet is an interactive menu-driven program for exploratory analysis 
and display of discrete and continuous multivariate network data. It has 
context-sensitive, interactive, on-line help, and always presents a 
color graphic representation of the data or the results of analysis. All 
graphics can be saved as bitmap or PostScript files. The program does 
ordinary univariate descriptive statistics, crosstabulation, analysis of 
variance, regression, and correlation. It also does network versions of 
crosstabulation, anova, correlation-regression in which it combines data 
that describes nodes with data that describes relationships between 
nodes into a single analytic model. It lets you mix node variables with 
link variables in a variety of kinds of analysis to explore the patterns 
in your network. While most network programs perform one or another type 
of structural analysis, MultiNet also does contextual analysis: it looks 
at attributes of people in the context of the relationships between and 
among them, and it looks at characteristics of relationships between 
people in the context of the attributes of the people. It is very happy 
with both ego-centric and ordinary whole-network data. It can easily 
deal with data that has many variables describing attributes of nodes 
and many that describe relationships between nodes.

The program has a variety of flexible data manipulation capabilities. It 
can handle missing data. It performs continuous and discrete 
transformations, such as ordination, quantiles, recategorization. Sets 
of ranked variables can be inverted. It does 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 capabilities. It can do 
four types of eigen decomposition of networks with up to 5,000 nodes for 
spectral analysis with interactive graphical display of results in 1, 2, 
or 3 dimensions, including link direction and/or strength, node 
attribute labels, and more options for graphic representation of eigen 
analysis results. The results of eigen analysis are integrated with the 
rest of the program so coordinates in eigen space can be used as 
variables in any other analysis the program does. Results of eigen 
decomposition can be used to create partitions that identify clusters or 
sets of structurally equivalent nodes. MultiNet does p* analysis on 
networks with up to 5,000 nodes, with interactive graphical display of 
results. Results of eigen analysis can be used to improve p* fits when 
using block structures. There is no easier way to do eigen analysis and 
p* modeling of networks.  MultiNet made this picture: 
http://www.sfu.ca/~richards/Multinet/Pages/manual.htm

MultiNet wants two files of data:  one is an ordinary rectangular 
cases-by-variables file describing the nodes. Use as many variables as 
you want and use hundreds of thousands of nodes if you have the data. 
Each node may be a person, an organization, an event, a publication, an 
author, a symptom, a dietary item, etc., etc. Some nodes may be people 
and others may be things that people have or do or attend.... The second 
file describes relations between pairs of nodes. It is organized as a 
rectangular dyads-by-variables file, in which each line of data 
describes the connections between a pair of nodes. (The data should only 
describe the connections that are present:  if there is no connection 
between a specific pair of nodes, just don't say anything about that 
pair.) Use hundreds of variables if you wish -- each one describing a 
type of relationship or some characteristic of the interaction between 
the nodes that constitutes a relationship. These variables may be 
binary, scalar, or continuous. Links may be directed or undirected. The 
program can read data in csv files that most spreadsheet programs can 
produce.

An analysis may involve one or more variables from one of the files or 
variables from both files.

The MultiNet page is at 
http://www.sfu.ca/~richards/Multinet/Pages/multinet.htm   Among other 
things, this page includes information about how to obtain MultiNet.

For a more complete description than the one above, go to the URL 
mentioned above and click on "MultiNet quick start"

For the entire 309-page manual, go to the URL mentioned above and click 
on "MultiNet manual July, 2005"

with my best wishes,
Bill


Daniele Mascia wrote:

>*****  To join INSNA, visit http://www.insna.org  *****
>
>Dear Socnetters,
>we are conducing an analysis centered on the ego-networks of scientists working in several international scientific institutions. Our idea is that of investigating structural characteristics of the one-step neighborhood of the surveyed scientists.
>To date, we have found a few articles explaining concepts and charateristics related to the ego-network, but we are still in search of materials about the methodology to be used to deal with ego-network data. In particular, given a set of ego-networks collected in spreadshit editors, we would like to know which format is reccomended for importing such data in one of the available software for network analysis (e.g. UCINET, Pajek...), and how to conduce relative analyses.
>
>Thanks in advance for any comments.
>
>Best regards,
>
>Daniele Mascia
>Catholic University - Rome
>
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