***** To join INSNA, visit http://www.insna.org ***** 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 > >_____________________________________________________________________ >SOCNET is a service of INSNA, the professional association for social >network researchers (http://www.insna.org). To unsubscribe, send >an email message to [log in to unmask] containing the line >UNSUBSCRIBE SOCNET in the body of the message. > > > > > _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.