Trying to come up with a cool visualization for a small side-project, i was contemplating how to draw, or approximate, an object using networks. During my creative process i remembered my colleague and friend Piotr Sapiezynski once told me how he once did something similar (see here and here). Thinking his visualizations look absolutely stunning i tried to do my own version.
As Denmark is getting closer to the next elections the debate about refugees, migrants and their descendants has yet again resurfaced and is beginning to turn sour. Disillusioned with this development and trying to get my mind off the issue, i wondered what the average politician looked like. I was thinking about something along the lines of this work or something like this piece by Soumitra Agarwal. After a quick online search, where i was unable to find much work on faces of politicians, i decided to create my own. The basic idea is to take lot of portrait pictures, overlay them, and take their median.
It’s actually a quite simple process and the code is freely available on Soumitra’s github.
To start with the pictures; I scraped the faces of Danish politicians from the web page of the danish parliament. This gave me the following mosaic (below mosaic shows an example of some of the scraped images). The parliament has 179 members, composed of members from multiple political parties.
The fact that all images were taken from the same webpage meant that they all had the same dimensions - making my life easier. Normalizing the faces, however, is still required. This basically means that faces are centered and adjusted (using a pre-trained model) to fit within a specific region. Effectively, this standardizes faces and makes them comparable. Below image shows the normalized versions of faces.
After standardization we can overlay the images, calculate the median, and get the face of the average danish politician. Here we see that the average politician is white, has stereotypically masculine features, and wears glasses.
Dividing pictures into male and female politicians (at present no danish politicians identify as non-binary) we see that the average male politician again wears glasses, is clean-shaven and wears a dark suit. The average female politician has blue eyes, does not wear a suit, and is portrayed in front of a warmer background.
We can dig further down and separate portraits with respect to political affiliation to get the average face of each political party. Overall the portraits are very white - I’m actually shocked by the lack of diversity (this might actually explain some of the toxic discourse in parliament). It would be interesting to compare these images to the face of the average Dane - if you have an open collection of images or know where to find one of Danes please share them. I’m planning to expand this work and start looking into the average face of danish actors.
Trying to kill some time on a 4-hour long train ride I played around with simulating random walk in two dimensions. Coloring each walker with it’s own unique colors, the motion of individual walkers will more or less look like confused ants moving around on a piece of paper. Resembling the behavior illustrated below – see code below.
I like to keep track of my life; collecting data about random things–one of them happens to be my travel patterns. While visualizing my own travels I started to wonder what the global airport network might look like. I remember reading about the structure of the airport network in the architecture of complex weighted networks by A. Barrat et al. but the paper never visualized the network. To figure it out, I first needed some data, luckily OpenFlights.org has a database of routes as well as airports, which allows us to create some pretty nice looking visualizations (see above figure).
This Sunday while surfing the web I came across a figure depicting the Rössler attractor and while looking at it, it suddenly struck me that I have always seen it depicted from this specific angle. But what does it look like from other angles? Curious, I sat down, quickly wrote a python script to generate the dynamics, used Matplotlib to plot the figure from multiple angles, and ffmpeg to aggregate them into an animation (see below). One thing lead to another and soon I found myself reading about other strange attractors, such as Clifford attractors, and writing code to generate the figures you see above.
I received questions from a couple of people asking me how I drew the network featured on the cover of PNAS (read about it here). Well, this blogpost is for you, and anybody else.
We (Sune Lehmann, Arek Stopczynski and yours truly) recently published a paper in PNAS where we give our two cents on how to uncover meaningful, “fundamental”, social structures from temporal complex networks. In addition to submitting the paper we also sent some pictures along which we felt would look good on the cover of PNAS. As it turns out one of them was actually selected!
I was watching the season finale of Game of Thrones the other day and wondered—with so many characters in the series what does the interaction network look like? Well, as it turns out I was not the first person to get this thought. In fact A. Beveridge and J. Shan read through Storm Of Swords (third book in the series) and mapped all the interactions between characters, and released the data. You can read more about their cool project here. They are, further, planning to release data regarding the others books as well.
While finalizing my PhD I was asked, alongside Sune Lehmann, to author a popular article about networks by the magazine Kvant (danish journal for physics and astronomy). We wrote and submitted the piece and were fairly confident in our work. Nonetheless we were surprised when we were contacted by the editor who asked us for permission to use one of my figures for the cover! This is my first cover, and I gotta say, it feels awesome, next stop …. Nature :)
Have you ever wondered which areas of New York City are the most popular? You need not worry anymore, this little movie will answer your questions. The video shows the dynamics of pick-ups and drop-offs within a representative week. It is interesting to see how the popularity of areas changes over the course of a day, and how certain areas attract more attention during nighttime. To me the circadian patterns resembles a heartbeat.
One of the most iconic sights in New York are its Yellow cabs. They are ubiquitous and an important lifeline that tie the city and its inhabitants together. Understanding how cabs move around can give us new insights into how people travel within the city, how people use the city, and which neighborhoods are popular.
Is just around the corner! We have some cool results that hopefully should be published soon. Until then here are two teaser pics.
Since I as a kid watched my first world cup (1994), I have been hooked on football (or soccer as the Americans call it). Back then l I remember that almost every player used to wear Adidas Copa Mundials - a stylish, yet simple black leather boots with 3 white stripes.
Things are moving fast now. We just uploaded another paper to ArXiv. Check it out! Measuring large-scale social networks with high resolution
Just submitted a paper - Wohoo! Meanwhile until it is published you can find it on arXiv. The paper investigates usability of the Bluetooth sensor is as a proxy for real life face-to-face interactions. You can learn more about the data on the SensibleDTU homepage.
I will be giving a talk at the Niels Bohr Institute on December 4th. Topic will be “Social Contacts and Commnities”. It is based on the results and finding from the SensibleDTU project.
How do we as humans interact over the course of a day? The video shows proximity interactions for student participating in the SensibleDTU project for a randomly chosen 24-hour interval.