A book written by Margaret Robertson features a network image created with NOESIS on its cover. The author herself found our project web page and expressed her interest in using one of our network figures on the cover of her new book:
From the publisher's website:
Communicating Sustainability is a book of evidence-based strategies for making sustainability vivid, accessible, and comprehensible. To do this, it brings together research from a range of specialties including cognitive psychology, visual perception, communication studies, environmental design, interpretive exhibit design, interpretive signage, wayfinding, storytelling, courtroom litigation, information graphics, and graphic design to illustrate not only what approaches are effective but why they work as they do.
The topic of sustainability is vast and complex. It interconnects multiple dimensions of human culture and the biosphere and involves a myriad of systems and processes, many of which are too large, too small, too fast, or too slow to see. Many people find verbal explanations about all of this too abstract or too complicated to understand, and for most people the concepts of sustainability are regarded as quirky, peripheral, and not essential to everyday life. Yet the challenges of sustainability concern the very survival of most species of life on Earth, including the human species. In order for life as we know it to survive and thrive into the future, sustainability must become broadly understood—by everyone, not just activists or specialists. This book offers tools to help make complex systems and nuanced, abstract ideas concrete and comprehensible to the broadest range of people. The goal of communication, and of this book, is to build understanding.
Margaret Robertson is a member of the American Society of Landscape Architects (ASLA) and teaches at Lane Community College in Eugene, Oregon, USA, where she coordinates the Sustainability degree program.
About the book cover...
The image on the book cover represents a random network generated by NOESIS, our network data mining tool. Similar images can be recreated by generating random networks according to different network models. Once the network is created and the desired layout is obtained, the network image can be saved in both vector (SVG) and raster (PNG/JPG) image format. For press quality images, the SVG version is preferred, since it can be zoomed in to whatever resolution is needed. Obviously, the network itself can also be saved in its own file format (using standard formats such as GML or GraphML).
In particular, the network displayed on the book cover was generated using the NOESIS Analyzer tool just by creating a random network using the Barabasi-Albert preferential attachment model (from the main menu: Network > New... > Random network > Barabasi-Albert preferential attachment network). In this case, we created a random network with 1000 nodes and a single link for each new node (the Barabasi-Albert model is a model of network formation that requires both parameters).
In order to change the resulting network layout, you can easily use the NOESIS Analyzer drag&drop interface or resort to automatic layout algorithms. In order to obtain the network layout on the book cover, we used the Frutcherman-Reingold layout algorithm (from the main menu: View > Frutcherman-Reingold layout). If you find that the result is not aesthetically pleasant, you can always drag nodes to their preferred location. Just randomizing the network layout [View > Random layout] before trying again with any automated layout algorithm might also work well in practice. Once you are pleased with the result, the last step is to save an image displaying your network in PNG, JPEG, or SVG format (e.g. from the main menu: Network > Export > SVG image).
BTW, when you create a network (or open an existing one, from a GML or GraphML file), you can change its display style, node and link sizes, and so on using the options within the View menu, e.g. adding color according to node properties. In a just few minutes, you can obtain impressive visualizations... in case you want to improve the design of your visual display of a network dataset (or a book cover ;-).