Learning UCINET
| Tutorial
Manuals & Guides Forum. Join the UCINET user's group where you can ask questions about how to use the program. |
NetDraw is a free program written by Steve Borgatti for visualizing both 1-mode and 2-mode social network data. It can handle multiple relations at the same time, and can use node attributes to set colors, shapes, and sizes of nodes. Pictures can be saved in metafile, jpg, gif and bitmap formats.
The program reads UCINET system files, UCINET DL files, Pajek files, and its own VNA format (which allows saving network and attribute data together, along with layout information like spatial coordinates, colors, etc.). IMPORTANT: If you format data as a VNA file, NETDRAW can handle large files. For example, sparse networks of 3500 nodes are very practical on a machine with 1GB of RAM (more is better). 10,000 nodes works fine with 2GB of RAM (assuming it is very sparse, of course).
Please make sure that your UCINET program is calling the latest version of NETDRAW. You can ensure this in UCINET by going to the OPTIONS menu and choosing "Helper Applications".
UCINET is a social network analysis program developed by Steve Borgatti, Martin Everett and Lin Freeman. The program is distributed by Analytic Technologies. UCINET works in tandem with freeware program called NETDRAW for visualizing networks. NETDRAW is installed automatically with UCINET.
A free trial version is available (very useful for giving workshops!). The trial version is identical to the regular version -- except that it has an expiration data.
Capabilities
UCINET is a comprehensive package for the analysis of social network data as well as other 1-mode and 2-mode data. Can read and write a multitude of differently formatted text files, as well as Excel files. Can handle a maximum of 32,767 nodes (with some exceptions) although practically speaking many procedures get too slow around 5,000 - 10,000 nodes. Social network analysis methods include centrality measures, subgroup identification, role analysis, elementary graph theory, and permutation-based statistical analysis. In addition, the package has strong matrix analysis routines, such as matrix algebra and multivariate statistics.
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