Complex Network Visualizations As A Means Of Generative Research In Design

The search for a possible design question, or generative research, is problematic. Generative research in design often relies on an unseen, intangible spark of intuition that leads to a novel design approach, and not many scholars or clients appreciate (or trust) the abstract nature of this process. Gathering information like demographics is useful data, but it only provides comparative information. Though charts and graphs can make apparent what is already true in the num- bers, they cannot reveal much more than that. They cannot, for example, reflect how a user population behaves, interacts, socializes, or moves. Consequently, the ways in which we navigate our world though visual communication, electronic or otherwise, is an increasingly challenging design problem. Seeing clear patterns for behavior is especially important in interaction design. Visual representations of pattern phenomena are possible with network science. The United States National Research Council defines network science as “the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena.” (Wikipedia, 2013). Most visualizations of complex networks are literally represented as lines connecting dots, the dots as data points and the lines as relationships. Carefully choosing the best data points, based on meaningful relationships, and applying good information design technique, makes a more comprehensive view of a designated design problem possible. A network visualization can be dynamic and three dimensional, though meaningful compositional view of a two-dimensional model can suffice. Because the data are visual, design decisions are more clearly communicated to both the designer and her audience. While it is possible to analyze the complex network with various mathematical functions—like density, clustering and connectedness—through these types of visualizations, a few simple examples show how powerful network science models can be.fals