My favourite papers of 2018

2018 has been a really exciting year, scientifically speaking a lot of new interesting studies have been published this year (so many that I have had a hard time keeping up with my to-read-list), and personally it has been a fruitful year where I was lucky to publish in PNAS and Nature Human Behaviour. Here i have included my favorite scientific papers of 2018.

My top reads

  1. The preeminence of ethnic diversity in scientific collaboration. Do diverse teams perform better? Yes! Studying the publication patterns of 6 million scientists AlShebli et al. find that papers with ethnically diverse teams have a 10% gain in impact woah....... This is of course correlation, not causation, but it highlights the important fact that diversity is a valuable asset. I would say this is not only case in scientific publishing but for every occupation - especially government .

  2. The strength of long-range ties in population-scale social networks. A pretty recent (published on December 21st), but nonetheless stunning piece of work. It’s probably my favorite network science publication of the year. It deals with the strength of social ties and how they decay over network distance (bear in mind this can be different from geographical distance). We generally imagine that our closest and strongest ties are with people in our immediate social circles and the further away we move they become weaker. Actually Park et al. shows us that this is not true. Your ties do become weaker as network distance increases but there is a tipping point at which your ties stop decaying and in some cases become stronger. Park and co-authors even discover something they call wormholes (see figure borrowed from their paper below), which are extremely strong ties that span large network distances. And they find these wormholes in 11 culturally diverse population-scale networks, from twitter to phone networks.

  3. Macroeconomic evidence suggests that asylum seekers are not a “burden” for Western European countries. A pretty thought provoking piece of work, especially seen in light of current anti-immigration populist agendas in Europe. Applying methodologies traditionally used to estimate the macroeconomic effects of shocks, d’Albis et al. show that inflows of asylum seekers do not deteriorate host countries’ economic performance. Instead, they find that the increase in public spending induced by asylum seekers is more than compensated for by an increase in tax revenues. Further, as asylum seekers become permanent residents, their economic impacts become even more positive. Unfortunately, this paper has not gotten the publicity it most definitely deserves.

  4. All-optical machine learning using diffractive deep neural networks. This is an interesting proof of concept. Basically Lin et al. show that it’s possible to 3D print a deep learning architecture (see image below) which using diffraction from multiple physical layers can classify handwritten digits with 90% accuracy. How cool is that!? However, I have tons of questions for this paper (while my knowledge of optics is a bit rusty) I remember that wavefronts are superimposable (meaning they are additive), so how this can be used as a non-linear classifier is unclear to me.

  5. Inferring Mechanisms for Global Constitutional Progress. A phenomenal publication by Rutherford et al. analyzing all the worlds constitutions and identifying similarities. Looking at temporal patterns they find an adoption hierarchy, which is a general pattern of how constitutions develop over time. Fundamental provisions about how to run a country are encoded first, then general rights are added and only then are rights for minority groups such as children added. For non-governmental and international organizations, such as UNICEF, which spend great resources on advocacy this knowledge is vital because it tells you that if you want to advocate for the rights of the most vulnerable you might need to package it with advocacy efforts for general rights. As an example, if you are trying to push for children’s rights, data shows that they never come before the right to unionize, you will achieve better results by bundling them together. You should also check out their interactive page that lets you explore the data further.

  6. Analyzing gender inequality through large-scale Facebook advertising data. Identifying inequalities, especially with respect to gender, is an important first step in achieving the sustainable development goals (particularity goal 5) and in building a more equitable society. However, gathering such data can be difficult. In this paper Garcia et al. demonstrate how the ad API of the 3rd most evil company in the world, Facebook (my personal top three: Palantir is first, second is McKinsey), can be used to proxy real inequalities with respect to education, health, and economic opportunity. They show that gender divides on Facebook explain various aspects of worldwide gender inequalities. This paper is an excellent example of how ingenious use of “small-data” can give us new astonishing insights.

Honorable mentions

  • Upper‐Body Strength and Political Egalitarianism: Twelve Conceptual Replications. How do you form your political views? By logic and reason alone, or does something else influence you? The research is founded on well-studied conflict behaviour in animals where physical strength shapes behavior. E.g. if an animal is stronger than their rivals they are prone to asserting themselves in the struggle for status and resources, while if they are weaker, they prefer to withdraw from conflict. Petersen and Laustsen show that modern men (not women) apply the same logic. They find that upper-body strength correlates positively with support for inequality WTF. This is the first time I have read a political psychology paper, but it will most certainly not be the last.

  • A large impact crater beneath Hiawatha Glacier in northwest Greenland. Imagine discovering a hidden 31-kilometer wide meteor crater beneath a 2 kilometers thick ice sheet from an impact that might have occurred as little as 13.000 years ago. While I have to admit the actual paper was too technical for me to fully understand this nice write-up in Science explains it well. It basically reads like an Indiana Jones on ice adventure. This meteor impact is a possible explanation for the Younger Dryas period, a sudden approx. 1000 year long cold dry period right after the last ice age, an event that has been speculated to have forced humans to start cultivating grasses and grains as other foods sources were dwindling. Also check out the below 3 min YouTube video about the paper.

  • Word embeddings quantify 100 years of gender and ethnic stereotypes. Can we use word embeddings to understand and quantify stereotypes over time? Why of course we can, we just need text data from the past 100 years and then its straightforward to quantify biases regarding gender, ethnicity, and cultural stereotypes. In their publication Garg et al. only look into stereotypes portrayed in the US. A similar study using text data from other countries/empires, especially Britain, might uncover other latent biases.

  • Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity. Are we shaped by our environment or do we shape it? While this paper does not look into establishing a causal link Maharana and Nsoesie show a strong correlation between features extracted from satellite image using Deep Learning and the prevalence of obesity. This is such a cool paper that basically shows that we can detect obesity from space .

The generic face of danish politics

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.

Transforming images into networks

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.

Random walk (art)

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.

Global Airport Network

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 has a database of routes as well as airports, which allows us to create some pretty nice looking visualizations (see above figure).

Strange Attractors

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.

Cover of PNAS - code

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.

On the cover of PNAS!!!

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!

Game Of Thrones network visualization

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.

On the cover of KVANT

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 :)

NYC Taxi - Heartbeat of NYC

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.

NYC Taxi - Statistics

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.

The next big thing....

Is just around the corner! We have some cool results that hopefully should be published soon. Until then here are two teaser pics.

World Cup 2014

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.


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.

Talk @ KU (4th Dec)

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.

Bluetooth Network

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.