Tuesday, September 27, 2016

Drawing graphs for publications or presentations

The one software I would not like to miss for drawing and editing graphs is yEd, a free software provided by yWorks. It is an intuitive tool to draw small graphs and networks where - in contrast to most normal vector graphics programs - moving around the nodes will automatically include the edges as well. Furthermore it is possible to mark a subgraph and to turn it or to mirror its position - without mirroring or turning the node labels as well.

 I've created almost all drawings for my publications with it, especially those for my book and this blog. My normal design pipeline is to draw the graph in yEd and then either to save it directly as eps or pdf for inclusion in LaTeX documents or to first save it as an svg. Then I would give some finishing touches to the drawing in inkscape, from which it can then again be exported as eps and pdf.

Despite the fact that I've been using this tool now for more than 12 years, I just learned that it is also the best way to  create an image for Power Point presentations. Instead of exporting it to bmp which scales badly, try exporting it to emf. Scaling this format leads to much better results.

Monday, September 26, 2016

Foreword by Steve Borgatti

I was very happy to hear that Steve Borgatti agreed to write a foreword for my book. As I describe it in my introduction, his research was the starting point for my book. So, I'll hand over to him and copy his foreword here:

This is a delightful book. It’s so easy to read, you can almost accidentally learn quite a bit of network science without even noticing it. Written in a playful manner, it tends to enliven the brain rather than put it to sleep – quite a change from the usual pedantic tome. It’s a quirky book that does not try to be systematic. For example, it does not cover “community detection” (that’s cluster analysis to you social scientists). As a result, the book has a great deal of personality.
But what I really like about the book is the subtext. What it’s actually about, in my opinion, is how to think, and here, that means how to think with models. Most academics are very gullible when it comes to concepts outside their disciplines. Within their area, any new idea or phrasing is treated with withering skepticism, but outside their area, they adopt ideas with the speed of teenagers adopting slang or fashion. Thus, a management scholar hears about small worlds and clustering coefficients and immediately shoehorns them into their next study. A physicist learns about betweenness centrality and suddenly there are 500 papers that reference the idea. If the first paper associates betweenness with influential spreaders in the spread of a disease, all of the following papers do the same. If you internalize this book, you won’t make that mistake. You will realize that, although there is a sense in which network measures are tools like hammers, there is much more to them. Hammers work pretty much the way they work in any setting, but using a network measure implicitly entails fitting a model of how things work. And if the model doesn’t fit, the measure doesn’t either.
Curiously, although I associate model-based thinking with the physical sciences, my experience is that both physical and social scientists are equally likely to have this mindless, “pluginski” attitude about network concepts. Therefore, I think this book would be useful for both audiences. But since the content of the book is mostly drawn from what Katharina calls the “network science” field (as opposed to the “social network analysis” field), I’m guessing it will appeal mostly to budding physical scientists. Too bad, because if there was ever an introduction to network science that was especially suitable for social scientists, this is it. 
I look forward to seeing this in print.
Steve Borgatti

Foreword by Steve Borgatti for Katharina A. Zweig's book: "Network Analysis Literacy",  in print; (c) by Springer Wien, used with permission