***** To join INSNA, visit http://www.insna.org *****
Don’t forget to register for a workshop at the next Sunbelt conference. Workshops are nice places to learn a bit and meet people. And maybe you even enroll for mine – I’ve been told that it is a great one. ;)
Jürgen Pfeffer, Carnegie Mellon University
The ability to visualize social structures is one of the most obvious advantages of social network analysis. With network drawings, you are able to visualize analytical research results in a communicative way. Presenting outcomes is a common application of network visualizations, however they are not only for use at the end of a research project; they can also yield first impressions of data in a very early stage of this process. The precondition for effective information visualization and successful visual reasoning is the capability to draw “good” pictures. Although drawing networks is more complex than clicking the “Draw” button of a social network analysis tool, a basic knowledge of certain rules can increase the quality of your visualizations dramatically. These techniques, explored in this workshop, make visualizing networks into a craft rather than an art.
In this workshop, you will learn about different aspects of visualizing networks and about underlying visualization principles, giving you the ability to assess the quality of network visualizations and to draw better network pictures by yourself. This workshop is neither an introduction into a specific network analysis tool nor will we discuss technical details about layout algorithms or the like. Instead, this 3 hours session will cover the following topics:
- Fundamentals of information visualization
- Visual elements for drawing networks
- Multivariate information visualization with networks
- Communicating with colors
- Human perception
Participants are invited to bring their own network data in order to create meaningful and compelling network drawings for their publications and presentations (e.g. Sunbelt).
Juergen Pfeffer, Assistant Research Professor
School of Computer Science
Carnegie Mellon University