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Embracing the Hairball

One of the perennial challenges in visualizing complex networks is dealing with hairballs: how do you draw a network that is so large and densely interconnected that any full rendering of it tends to turn into an inscrutable mess? There are various approaches to addressing this problem: BioFabric, Hive Plots, and many others. Most involve very different visual abstractions for the network.

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Cowboys and Inventors: The Myth of the Lone Genius

I recently moved from Boston to Oklahoma City. My wife got offered a tenure-track position at the University of Oklahoma, which was too good an opportunity for her career for us to pass up. Prior to the move, I had done a lot of traveling in the US, but almost exclusively on the coasts, so I didn't know what living in the southern Midwest would bring, and I was a bit trepidatious. It has turned out to be a fantastic move. There is a thriving high-tech startup culture here. I've been able to hire some great talent out of the University, and we're now planning to build up a big Exaptive home office here. Even more important, I was delighted to find a state that was extremely focused on fostering creativity and innovation. In fact, the World Creativity Forum is being hosted here this week, and I was asked to give a talk about innovation. As I thought about what I wanted to say, I found myself thinking about . . . cowboys.

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The Data Scientific Method

The Oxford English Dictionary defines the scientific method as "a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses." With more scientists today than ever, the scientific method is alive and well, and generating more data than ever. This explosion of data has brought about the field of data science and an associated plethora of analytics tools. Controversially, some have claimed, such as in this Wired magazine article, that data science is so powerful that it has made the scientific method obsolete. Google's founding philosophy is that “we don't know why this page is better than that one. If the statistics of incoming links say it is, that's good enough.” The implication is that with enough data, people will no longer need to know why something happens, it just does, and that’s good enough. Is it, really?

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We work on Technology. Then it works on us.

I think I was 10 years old when my dad brought home our first microwave oven. It was an imposing black box that weighed a ton and had scary warning labels that mentioned radiation. The only time I had ever heard mention of radiation before was in regard to the atom bomb. We felt like we were supposed to run for cover whenever we turned it on, but, like everyone else I knew who had one, we did just the opposite. We huddled around it. We brought our noses right up to the translucent window, and watched, mesmerized in wonder, as the food inside got zapped by mysterious, limitless, invisible energy. When the timer beeped, and the door opened to reveal a steaming bowl of soup that had been cold only a minute ago, it seemed like a miracle. I remember those early days with the microwave vividly – experimenting with eggs, and chocolate syrup, and the off-limits gold-rimmed fine china that would send off an awe-inspiring barrage of orange sparks after just 15 seconds. Just 15 seconds! 15! I think that was the most important thing of all about the microwave oven – not what it did to my food, but what it did to my sense of time.

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