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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
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:
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
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,
Daniele Mascia wrote:
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>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.
>Catholic University - Rome
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