Michele Coscia - Connecting Humanities

Michele Coscia I am a post-doc fellow at the Center for International Development, Harvard University in Cambridge. I mainly work on mining complex networks, and on applying the extracted knowledge to international development and governance. My background is in Digital Humanities, i.e. the connection between the unstructured knowledge and the cold organized computer science. I have a PhD in Computer Science, obtained in June 2012 at the University of Pisa. In my career, I also worked at the Center for Complex Network Research at Northeastern University, with Albert-Laszlo Barabasi. On this website you can browse my papers, algorithms and datasets with the top navigation, or simply skim my blog posts that briefly present my topics and papers below this box.

22 August 2016 ~ 0 Comments

It’s Not All in the Haka: Networks Matter in Rugby Too

If there is a thing that I love more than looking at silly pictures on the Interwebz for work is to watch rugby for work. I love rugby: in my opinion it is the most beautiful team sport out there. It tingles my network senses: 15 men on the field have to coordinate like a single organism to achieve their goal — crossing the goal line with the ball by passing it backwards instead of forward. When Optasports made available some data collected during 18 rugby matches I felt I could not miss the opportunity for some hardcore network nerding on them. The way teams weave their collaboration networks during a match must have some relationship with their performance, and I was going to find out what this relationship might be.


For my quest I teamed up with Luca Pappalardo and Paolo Cintia, two friends of mine who are making an impact on network and big data sports analytics, both in soccer and in cycling. The result was “The Haka Network: Evaluating Rugby Team Performance with Dynamic Graph Analysis“, a paper recently presented at the DyNo workshop in San Francisco. Our questions were:

  1. Is there a relationship between the topology of the network of passes and the success of the team?
  2. Is there a relationship between disruptions made by tackles and territorial gains?
  3. If we want to predict a team’s success, is it better to build networks of passes and disruptions for each action separately or for the entire match?
  4. Can we use these relationships to “predict” the outcome of the match?


A passage network is simply a network whose nodes are the players of a team and the directed connections go from the player originating a pass to the player receiving the ball. We consider only completed passages: the ones that did not result in an error or lost possession. In the above picture, those are the green edges and they are always established between players belonging to the same team. In rugby, players are allowed to tackle the current ball carrier of the opponent team. When that happens, we create another directed edge, this time in what we call “disruption network”. The aim of a tackle is to prevent the opponent team from gaining meters. These are the red edges in the above picture and can only be established between players belonging to opposite teams. The picture you see is the collection of all passes and tackles which happened in the Italy vs New Zealand match in 2012. It is a multilayer network as it contains edges of two different types: passes and tackles.

Once we have pass and disruption networks we can calculate a collection of network measures. I’ll give a brief idea here, but if you are looking for more formal definitions you’ll have to search for them in the paper:

  • Connectivity: how many pass connections you have to remove to isolate players;
  • Assortativity: the tendency of players to pass the ball to players with a similar number of connections — in high assortativity central players pass to other central players and marginal players to other marginal players;
  • Components: how many “sinks” there are, in that the ball never goes back to the bulk of the team when it is passed to a player in a sink;
  • Clustering: how many triangles there are, meaning that the team can be decomposed in many different smaller sub-teams of three players.

These are the features we calculated for the pass networks. The disruption case is slightly different. We calculated the same features for the team when removing the tackled player, weighted on the relative number of tackles. If 50% of the tackles hit player number 11, then 50% of the disrupted connectivity is the connectivity value of the pass network when removing player 11. The reason is that the tackled player is temporarily removed from the game, so we need to know how the team performs without him, weighted on the number of times this occurrence happens.

So, it is time to give some answers. Shall we?

1. Is there a relationship between the topology of the network of passes and the success of the team?


Yes, there is. We calculate “success” as the number of meters gained, ball in hand, by the team. The objective of rugby is to cross the goal line carrying the ball, so meters made is a pretty good indicator. We control for two things. First, the total number of passes: it simply means the team was able to hold onto the ball longer, so it is trivially expected to result in more meters. Second, the home advantage, which is a huge factor in rugby: Italy won only 12 out of 85 matches in the European “Six Nations” tournament, and 11 of them were in Italy. After these controls, we find that two features have good correlations with meters made: connectivity and components. The more edges are needed to isolate a player, the more meters a team is expected to make (p < .01, R2 = 47%). More sinks in a team is associated with lower gains in meters.

2. Is there a relationship between disruptions made by tackles and territorial gains?


Again: yes. In this case it seems that all calculated features matter to predict meters made. The strongest factor is again leftover connectivity. It means that if the connectivity of the pass network increases after the tackled player is removed from it then the team is able to advance more. Simplifying: if you are able to tackle only low connectivity players, then your opponent is able to gain more territory (p < .01, R2 = 48%).

3. Is it better to build networks of passes and disruptions for each action separately or for the entire match?

The answer to the previous two questions were made by calculating the features on the global match networks. The global network uses all the data from a match, exactly like the pass and disruption edges depicted in the above figure. In principle, one could calculate these features as the match unfolds: sequence by sequence. In fact, networks features at the action level work very well in soccer, as Luca and Paolo already proved. Does that work also in rugby?

Surprisingly, the answer is no. We recalculated the features for each passage of play. A passage of play is the part of a match from when a team gets into possession of the ball until it loses it, scores, or the game flow stops for an infraction. When we calculate features at this level, we find very weak correlations: almost nothing is significant and, when it is, the predictive power is very low. We think that this is because in rugby our definition of sequence is too strict. While soccer is a tactical game — where each sequence counts for itself — rugby is a grand strategy game: sequences build cumulative advantage which pays off after a series of them — or only in the match as a whole.

4. Can we use these relationships to “predict” the outcome of the match?


This is the real queen question of the post, and we do not fully answer it, unfortunately. However, we have a very good reason to think that the answer could be positive. We created a predictor which trains on 17 matches and then, given the global multi-layer network, will pick the winner. You can see the problem of the approach here: we use the network of the match as it happened to “predict” the outcome. However, we did that only because we did not have enough matches for each individual team: we believe we can first predict how pass and disruption networks will shape in a new match using historic data and then use that to predict the outcome. That will be future works, maybe if some team is intrigued by networks and wants to contact us for a collaboration… (wink wink).

The reason I still like to report on our predictor is that it has a very promising property. Its accuracy was 83%. We compared with a prediction made with official rugby rankings, whose performance is worse: 76% accuracy. We also tested against bookmakers, who are better than us with their 86% accuracy. However, historic data on bets only cover more important matches — only 14 out of 18 — and matches between minor teams are usually less predictable. The fact that we are on par on a more difficult task is remarkable. More importantly, bookies tend to just “choose the best team”. For instance, they always predict a New Zealand win. The Haka, however, is not always enough and our networks caught that. New Zealand lost to England in a big upset on December 1st 2012. The bookmakers didn’t see that coming, but our network approach could have.

07 July 2016 ~ 0 Comments

Building Data-Driven Development

A few weeks ago I had to honor to speak at my group’s  “Global Empowerment Meeting” about my research on data science and economic development. I’m linking here the Youtube video of my talk and my transcript for those who want to follow it. The transcript is not 100% accurate given some last minute edits — and the fact that I’m a horrible presenter :-) — but it should be good enough. Enjoy!

We think that the big question of this decade is on data. Data is the building blocks of our modern society. We think in development we are not currently using enough of these blocks, we are not exploiting data nearly as much as we should. And we want to fix that.

Many of the fastest growing companies in the world, and definitely the ones that are shaping the progress of humanity, are data-intensive companies. Here at CID we just want to add the entire world to the party.

So how do we do it? To fix the data problem development literature has, we focus on knowing how the global knowledge building looks like. And we inspect three floors: how does knowledge flow between countries? What lessons can we learn inside these countries? What are the policy implications?

To answer these questions, we were helped by two big data players. The quantity and quality of the data they collect represent a revolution in the economic development literature. You heard them speaking at the event: they are MasterCard – through their Center for Inclusive Growth – and Telefonica.

Let’s start with MasterCard, they help us with the first question: how does knowledge flow between countries? Credit card data answer to that. Some of you might have a corporate issued credit card in your wallet right now. And you are here, offering your knowledge and assimilating the knowledge offered by the people sitting at the table with you. The movements of these cards are movements of brains, ideas and knowledge.

When you aggregate this at the global level you can draw the map of international knowledge exchange. When you have a map, you have a way to know where you are and where you want to be. The map doesn’t tell you why you are where you are. That’s why CID builds something better than a map.

We are developing a method to tell why people are traveling. And reasons are different for different countries: equity in foreign establishments like the UK, trade partnerships like Saudi Arabia, foreign greenfield investments like Taiwan.

Using this map, it is easy to envision where you want to go. You can find countries who have a profile similar to yours and copy their best practices. For Kenya, Taiwan seems to be the best choice. You can see that, if investments drive more knowledge into a country, then you should attract investments. And we have preliminary results to suggest whom to attract: the people carrying the knowledge you can use.

The Product Space helps here. If you want to attract knowledge, you want to attract the one you can more easily use. The one connected to what you already know. Nobody likes to build cathedrals in a desert. More than having a cool knowledge building, you want your knowledge to be useful. And used.

There are other things you can do with international travelers flows. Like tourism. Tourism is a great export: for many countries it is the first export. See these big portion of the exports of Zimbabwe or Spain? For them tourism would look like this.

Tourism is hard to pin down. But it is easier with our data partners. We can know when, where and which foreigners spend their money in a country. You cannot paint pictures as accurate as these without the unique dataset MasterCard has.

Let’s go to our second question: what lessons can we learn from knowledge flows inside a country? Telefonica data is helping answering this question for us. Here we focus on a test country: Colombia. We use anonymized call metadata to paint the knowledge map of Colombia, and we discover that the country has its own knowledge departments. You can see them here, where each square is a municipality, connecting to the ones it talks to. These departments correlate only so slightly with the actual political boundaries. But they matter so much more.

In fact, we asked if these boundaries could explain the growth in wages inside the country. And they seem to be able to do it, in surprisingly different ways. If you are a poor municipality in a rich state in Colombia, we see your wage growth penalized. You are on a path of divergence.

However, if you are a poor municipality and you talk to rich ones, we have evidence to show that you are on a path of convergence: you grow faster than you expect to. Our preliminary results seem to suggest that being in a rich knowledge state matters.

So, how do you use this data and knowledge? To do so you have to drill down at the city level. We look not only at communication links, but also at mobility ones. We ask if a city like Bogota is really a city, or different cities in the same metropolitan area. With the data you can draw four different “mobility districts”, with a lot of movements inside them, and not so many across them.

The mobility districts matter, because combining mobility and economic activities we can map the potential of a neighborhood, answering the question: if I live here, how productive can I be? A lot in the green areas, not so much in the red ones.

With this data you can reshape urban mobility. You know where the entrance barriers to productivity are, and you can destroy them. You remodel your city to include in its productive structure people that are currently isolated by commuting time and cost. These people have valuable skills and knowhow, but they are relegated in the informal sector.

So, MasterCard data told us how knowledge flows between countries. Telefonica data showed the lessons we can learn inside a country. We are left with the last question: what are the policy implications?

So far we have mapped the landscape of knowledge, at different levels. But to hike through it you need a lot of equipment. And governments provide part of that equipment. Some in better ways than others.

To discover the policy implications, we unleashed a data collector program on the Web. We wanted to know how the structure of the government in the US looks like. Our program returned us a picture of the hierarchical organization of government functions. We know how each state structures its own version of this hierarchy. And we know how all those connections fit together in the union, state by state. We are discovering that the way a state government is shaped seems to be the result of two main ingredients: where a state is and how its productive structure looks like.

We want to establish that the way a state expresses its government on the Web reflects the way it actually performs its functions. We seem to find a positive answer: for instance having your environmental agencies to talk with each other seems to work well to improve your environmental indicators, as recorded by the EPA. Wiring organization when we see positive feedback and rethinking them when we see a negative one is a direct consequence of this Web investigation.

I hope I was able to communicate to you the enthusiasm CID discovered in the usage of big data. Zooming out to gaze at the big picture, we start to realize how the knowledge building looks like. As the building grows, so does our understanding of the world, development and growth. And here’s the punchline of CID: the building of knowledge grows with data, but the shape it takes is up to what we make of this data. We chose to shape this building with larger doors, so that it can be used to ensure a more inclusive world.

By the way, the other presentations of my session were great, and we had a nice panel after that. You can check out the presentations in the official Center for International Development Youtube channel. I’m embedding the panel’s video below:

09 June 2016 ~ 0 Comments

Netsci 2016 Report


Another NetSci edition went by, as interconnected as ever. This year we got to enjoy Northeast Asia, a new scenario for us network scientists, and an appropriate one: many new faces popped up both among speakers and attendees. Seoul was definitely what NetSci needed at this time. I want to spend just a few words about what impressed me the most during this trip — well, second most after what Koreans did with their pizzas: that is unbeatable. Let’s go chronologically, starting with the satellites.

You all know I was co-organizing the one on Networks of networks (you didn’t? Then scroll down a bit and get informed!). I am pleased with how things went: the talks we gathered this year were most excellent. Space constraints don’t allow me to give everyone the attention they deserve, but I want to mention two. First is Yong-Yeol Ahn, who was the star of this year. He gave four talks at the conference — provided I haven’t miscounted — and his plenary one on the analysis of the Linkedin graph was just breathtaking. At Netonets, he talked about the internal belief network each one of us carries in her own brain, and its relationship with how macro societal behaviors arise in social networks. An original take on networks of networks, and one that spurred the idea: how much are the inner workings of one’s belief network affected by the metabolic and the bio-connectome networks of one own body? Should we study networks of networks of networks? Second, Nitesh Chawla showed us how high order networks unveil real relationships among nodes. The same node can behave like it is many different ones, depending on which of its connections we are considering.


Besides the most awesome networks of networks satellite, other ones caught my attention. Again, space is my tyrant here, so I get to award just one slot, and I would like to give it to Hyejin Youn. Her satellite was on the evolution of technological networks. She does amazing things tracking how the patent network evolved from the depths of 1800 until now. The idea is to find viable innovation paths, and to predict which fields will have the largest impact in the future.

When it comes to the plenary sessions, I think Yang-Yu Liu stole the spotlight with a flashy presentation about the microcosmos everybody carries in their guts. The analysis of the human microbiome is a very hot topic right now, and it pleases me to know that there is somebody working on a network perspective of it. Besides scientific merits, whoever extensively quotes Minute Earth videos — bonus points for it being the one about poop transplants — has my eternal admiration. I also want to highlight Ginestra Bianconi‘s talk. She has an extraordinary talent in bringing to network science the most cutting edge aspects of physics. Her line of research combining quantum gravity and network geometry is a dream come true for a physics nerd like myself. I always wished to see advanced physics concepts translated into network terms, but I never had the capacity to do so: now I just have to sit back and wait for Ginestra’s next paper.


What about contributed talks? The race for the second best is very tight. The very best was clearly mine on the link between mobility and communication patterns, about which I showed a scaling relationship connecting them (paperpost). I will be magnanimous and spare you all the praises I could sing of it. Enough joking around, let’s move on. Juyong Park gave two fantastic talks on networks and music. This was a nice breath of fresh air for digital humanities: this NetSci edition was orphan of the great satellite chaired by Max Schich. Juyong showed how to navigate through collaboration networks on classical music CDs, and through judge biases in music competitions. By the way, Max dominated — as expected — the lighting talk session, showing some new products coming from his digital humanities landmark published last year in Science.  Tomomi Kito was also great: she borrowed the tools of economic complexity and shifted her focus from the macro analysis of countries to the micro analysis of networks of multinational corporations. A final mention goes to Roberta Sinatra. Her talk was about her struggle into making PhD committees recognize that what she is doing is actually physics. It resonates with my personal experience, trying to convince hiring committees that what I’m doing is actually computer science. Maybe we should all give up the struggle and just create a network science department.

And so we get to the last treat of the conference: the Erdos-Renyi prize, awarded to the most excellent network researcher under the age of 40. This year it went to Aaron Clauset, and this pleases me for several reasons. First, because Aaron is awesome, and he deserves it. Second, because he is the first computer scientist who is awarded the prize, and this just gives me hope that our work too is getting recognized by the network gurus. His talk was fantastic on two accounts.


For starters, he presented his brand new Index of Complex Networks. The interface is pretty clunky, especially on my Ubuntu Firefox, but that does not hinder the usefulness of such an instrument. With his collaborators, Aaron collected the most important papers in the network literature, trying to find a link to a publicly available network. If they were successful, that link went in the index, along with some metadata about the network. This is going to be a prime resource for network scientists, both for starting new projects and for the sorely needed task of replicating previous results.

Replication is the core of the second reason I loved Aaron’s talk. Once he collected all these networks, for fun he took a jab at some of the dogmas of networks science. The main one everybody knows is: “Power-laws are everywhere”. You can see where this is going: the impertinent Colorado University boy showed that yes, power-laws are very common… among the 5-10% of networks in which it is possible to find them. Not so much “everywhere” any more, huh? This was especially irreverent given that not so long before Stefan Thurner gave a very nice plenary talk featuring a carousel of power laws. I’m not picking sides on the debate — I feel hardly qualified in doing so. I just think that questioning dearly held results is always a good thing, to avoid fooling ourselves into believing we’ve reached an objective truth.


Among the non-scientific merits of the conference, I talked with Vinko Zlatic about the Croatian government on the brink of collapse, spread the search for a new network scientist by the Center for International Development, and discovered that Korean pizzas are topped with almonds (you didn’t really think I was going to let slip that pizza reference at the beginning of the post, did you?). And now I made myself sad: I wish there was another NetSci right away, to shove my brain down into another blender of awesomeness.  Oh well, there are going to be plenty of occasions to do so. See you maybe in Dubrovnik, Tel-Aviv or Indianapolis?

20 May 2016 ~ 0 Comments

Program of Netonets 2016 is Out!

As announced in the previous post, the symposium on networks of networks is happening in less than two weeks: May 31st @ 9AM, room Dongkang C of the K-Hotel Seoul, South Korea. Przemek Kazienko, Gregorio D’Agostino and I have a fantastic program and set of speakers to keep you entertained on multilayer, interdependent and multislice networks. Take a look for yourself!

Session I

9:00 – 9:15: Room set up
9:15 – 9:30: Welcome from the organizers
9:30 – 10:15: Invited I: Yong-Yeol Ahn: Dynamics of social network of belief networks
10:15 – 11:00: Invited II: Luca Maria Aiello: The Nature of Social Links

11:00 – 11:30: Coffee Break

Session II

11:30 – 12:15: Invited III: Jianxi Gao: Networks of Networks: From Structure to Dynamics
12:15 – 13:00: Invited IV: Tomasz Kajdanowicz: Fusion methods for classification in multiplex networks

13:00 – 14:30: Lunch Break

Session III

14:30 – 15:15: Invited V: Michael Danziger: Beyond interdependent networks
15:15 – 15:35: Contributed I: Bruno Coutinho: Greedy Leaf Removal on Hypergraphs
15:35 – 15:55: Contributed II: Yong Zhuang: Complex Contagions in Clustered Random Multiplex Networks

15:55 – 16:30: Coffee Break

Session IV

16:30 – 17:15: Invited VI: Nitesh Chawla: From complex interactions to networks: the higher-order network representation

17:15 – 18:00: Round table – Open discussion
18:00 – 18:15: Organizers wrap up

Remember to register to the main NetSci conference if you want to attend.

Incidentally, the end of May is going to be a rather busy period for me. Besides co-organizing Netonets and speaking at the main Netsci conference, I’m going to present also at the Core50 conference in Louvain-la-Neuve, Belgium, on the role of social and mobility networks in shaping the economic growth of a country. Thanks to Jean-Charles Delvenne for inviting me!

I hope to see many of you there!

17 March 2016 ~ 0 Comments

Networks of Networks @ NetSci 2016

EDIT: Deadlines & speakers updated. Submission deadline is on April 27th, notification on April 29th.


Dear readers of this blog — yes, both of you –: it’s that time of the year again. As tradition dictates, I’m organizing the Networks of Networks symposium, satellite event of the NetSci conference.

Networks of networks are structures in which the nodes may be connected through different relations. They can represent multifaceted social interaction, critical infrastructure and complex relational data structures. In the symposium, we are looking for a diversity of research contributions revolving around networks of networks of any kind: in social media, in infrastructure, in culture. The call for contributed talks is OPEN, and you can submit your abstract here: https://easychair.org/conferences/?conf=non2016

The deadline for submissions is April 15th, 2016 April 27th, 2016, just a month from now. We will notify acceptance by April 22nd, 2016 April 29th, 2016.

Here’s my handy guide to few of the many reasons to come:

  • Networks of networks are awesome, a hot topic in network science and a lot of super smart people work on them. You wouldn’t pass the opportunity to mingle with them, would you?
  • We have a lineup of outstanding confirmed keynotes this year — truth to be told, we have that every year:
  • This year NetSci will take place at the K-Hotel, Seoul, Korea (South, whew…). You really should not miss this occasion to visit such fascinating place.

The Networks of Networks symposium will be held on May 31st, 2016. The full conference, including all satellites, runs from May 30th to June 3rd. You can find all relevant information for the conference in the official NetSci website. Our symposium has a website too: check it out. In it, you will find also the fundamental information about all the people organizing this event with me: without them none of this would be possible. Here they are:

And also a list of other people, helping with their ideas, time and enthusiasm:

  • Matteo Magnani
  • Ian Dobson
  • Luca Rossi
  • Leonardo Duenas-Osorio
  • Dino Pedreschi
  • Guido Caldarelli
  • Vito Latora

Hope to see many of you in Korea!

16 February 2016 ~ 0 Comments

Data Trips Diary: Bogotá

My last post on this blog was about mobility in Colombia. For that study, I had the opportunity of dunking my hands into a bag filled with interesting data. To do so, I traveled to Bogotá. It is a fascinating place and I decided to dedicate this post to it: what the city looks like under the lens of some simple mobility and economic data analysis. If in the future I will repeat the experience somewhere else I will be more than happy to make this a recurrent column of this blog.

The cliché would demand from me a celebration of the chaos in Bogotá. After all, we are talking about one of the top five largest capitals in Latin America, the chaos continent par excellence. Yet, your data goggles would tell you a different story. Bogotá is extremely organized. Even at the point of being scary. There is a very strict division of social strata: the city government assigns each block a number from 1 (poorest) to 6 (richest) according to its level of development and the blocks are very clustered and homogeneous:


In the picture: red=1, blue=2, green=3, purple=4, yellow=5 and orange=6 (grey = not classified). That map doesn’t seem very chaotic to me, rather organized and clustered. One might feel uneasy about it, but that is how things are. The clustering is not only on the social stratum of the block, but also in where people work. If you take a taxi ride, you will find entire blocks filled with the very same economic activities. Not knowing that, during one of my cab rides I thought in Bogotá everybody was a car mechanic… until we got passed that block.

The order emerges also when you look at the way the people use the city. My personal experience was of incredulity: I went from the city hall to the house of a co-worker and it felt like moving to a different city. After a turn left, the big crowded highway with improvised selling stands disappeared into a suburb park with no cars and total quiet. In fact, Bogotá looks like four different cities:


Here I represented each city block as a node in a network and I connected blocks if people commute to the two places. Then I ran a community discovery algorithm, and plotted on the map the result. Each color represents an area that does not see a lot of inter-commutes with the other areas, at least compared with its own intra-commutes.

Human mobility is interesting because it gives you an idea of the pulse of a place. Looking at the commute data we discovered that a big city like Bogotá gets even bigger during a working day. Almost half a million people pour inside the capital every day to work and use its services, which means that the population of the city increases, in a matter of hours, by more than 5%.


It’s unsurprising to see that this does not happen during a typical Sunday. The difference is not only in volume, but also in destination: people go to different places on weekends.


Here, the red blocks are visited more during weekdays, the white blocks are visited more in weekends. It seems that there is an axis that is more popular during weekdays — that is where the good jobs are. The white is prevalently residential.

Crossing this commute information with the data on establishments from the chamber of commerce (camara de comercio), we can also know which businesses types are more visited during weekends, because many commuters are stopping in areas hosting such businesses. There is a lot of shopping going on (comercio al por menor) and of course visits to pubs (Expendio De Bebidas Alcoholicas Para El Consumo Dentro Del Establecimiento). It matches well with my personal experience as, once my data quests were over, my local guide (Andres Gomez) lead me to Andres Carne de Res, a bedlam of music, food and lights, absolutely not to be missed if you find yourself in Bogotá. My personal advice is to be careful about your beverage requests: I discovered too late that a mojito there is served in a soup bowl larger than my skull.

Most of what I wrote here (minus the mojito misadventure) is included in a report I put together with my travel companion (Frank Neffke) and another local (Eduardo Lora). You can find it in the working paper collection of the Center for International Development. I sure hope that my data future will bring me to explore other places as interesting as the capital of Colombia.

15 January 2016 ~ 0 Comments

The Limited Power of Telecommunication

As a kid from the 80s*, I remember how revolutionary the cellphone era was. It happened so fast. It seemed that, overnight, you could carry in your pocket a device connecting you to everybody you knew, no matter how far. To me, it changed everything. But did it? Yes, over-apprehensive parents can check their babies at the swipe of a finger, and whoever does not carry their cellphone with themselves at all times is labeled as a weirdo — I’m guilty of that. But the telecommunication revolution promised something more: the elimination of distance in communication. Did it deliver? This question was the motivation engine for the paper “Evidence That Calls-Based and Mobility Networks Are Isomorphic” which I wrote with my boss Ricardo Hausmann and which recently appeared in PLoS One.

The question is rather daring, so we decided to take it step by step. The simplest thing we came up was: let’s draw a map of cellphone calls and see if it looks like a geographical map. If it does, we might be onto something. To do so, we obtained data from telecommunication operators in Colombia. They provided us call detail records, where identifiers were encrypted to preserve the anonymity of the people making and receiving the calls. We also aggregated the data to make even the slightest re-identification impossible: every ID was associated to the municipality in which it spent most of its time and so all data was lumped together at the municipality level. At this point, we could draw a map of which municipalities had a significant call traffic with one another. This we called the “Call-based” network:


Click to enlarge

Before jumping to conclusions with this picture, we built a sister network. Since we just said we knew the location of a phone when making a call, we can keep a record of the different municipalities where we spotted the phone. Again, we joined together all data at the municipality level. This sister network is then a “Mobility” network of Colombia:


Click to enlarge

It seems there’s something here. The two networks appear to be similar: Bogotá seems to be a prominent center and the connections have a geographical component embedded into them. To make this more evident, we drew the networks on a Colombian map. The color of the municipalities is the same color of the nodes in the pictures above: nodes with the same color are very related in the network — network clusters.


Click to enlarge

The call-based network is on the left, the mobility is on the right. Blocks of the same color on the left are a clear indication of the call connections being influenced by geography. If there was no relation, the map would look like the Harlequin shirt, with colors scattered evenly across the territory. Mobility clusters are also short-range, although the pattern is harder to see because I had to use many more colors: the clusters are smaller. But the two networks are closely related: in fact, the larger call-based clusters contain the smaller mobility ones, as we show in the paper. We can say that there is a strong relationship between calls and mobility.

This is nice, because it fits with many works in computer science that actually use social relationships to predict human mobility… and vice versa. On the other hand, it is not nice because the existence of these papers also tells us ours is not a new result. Moreover, my starting point was to hint that the call-based and mobility networks are obeying the same laws, not that they are merely correlated. We need to go a step further.

Our step was to consider the difference that distance makes in the two networks. When looking at mobility, the distance between an origin and a destination is an important cost. In the call-based networks, things are a bit trickier. If modern telecommunication really delivered what it promised, distance should be a really low cost, and probably non-linear. To start a social relationship it is not needed to be in the same place at any given time, and even if we move to opposite ends of the world, we can still call each other. As a consequence, there shouldn’t be a way to scale the cost of distance in the call-based network to look like the one in the mobility network.

When we attempted to perform such scaling, we discovered it was actually possible. We checked, at any given distance, the ratio between commuters and callers. If two municipalities are at 50km distance, and there are twice as many commuters than callers, we have a dot on coordinates (50, 2). If we take two municipalities at 100km distance, and the commuters are just a third of the number of callers, the data point is at coordinates (100, .33). Once we consider all data points, we can fit our green line, AKA the scaling function from calls to mobility:


When we used this adjustment to calculate new call-based clusters using the distance cost “as if” it was the mobility network, we obtained the mobility clusters. We detail in the paper the reasons why this is not as circular as it seems.  In practice, our green line is a transformation function that morphs the call-based network into the mobility network. If modern telecommunication really killed distance, that green line shouldn’t exist, or at least it should be so wobbly to be practically useless.

There are many ways in which you could interpret this result. One that Ricardo and I like focuses on the relationship between face-to-face and electronic mediated meetings. It’s not like the people you call are the ones you really would rather meet but you cannot. It’s more like you call AND you meet, whenever it is possible. Face-to-face and electronic mediated meetings are not really substitutes in this world, they are more like complements. To come back to my opening, I’d say new technologies didn’t eliminate distance from the communication equation. Alleviate, yes. But ultimately, it’s more like an increased bandwidth than a revolution. At least so far.

* Shut up, I’m still in my twenties. Everybody knows 1996 was only 10 years ago.

18 November 2015 ~ 0 Comments

Evaluating Prosperity Beyond GDP

When reporting on economics, news outlets very often refer to what happens to the GDP. How is policy X going to affect our GDP? Is the national debt too high compared to GDP? How does my GDP compare to yours? The concept lurking behind those three letters is the Gross Domestic Product, the measure of the gross value added by all domestic producers in a country. In principle, the idea of using GDP to take the pulse of an economy isn’t bad: we count how much we can produce, and this is more or less how well we are doing. In practice, today I am jumping on the huge bandwagon of people who despise GDP for its meaningless, oversimplified and frankly suspicious nature. I will talk about a paper in which my co-authors and I propose to use a different measure to evaluate a country’s prosperity. The title is “Going Beyond GDP to Nowcast Well-Being Using Retail Market Data“, my co-authors are Riccardo Guidotti, Dino Pedreschi and Diego Pennacchioli, and the paper will be presented at the Winter edition of the Network Science Conference.

GDP is gross for several reasons. What Simon Kuznets said resonates strongly with me, as already in the 30s he was talking like a complexity scientist:

The valuable capacity of the human mind to simplify a complex situation in a compact characterization becomes dangerous when not controlled in terms of definitely stated criteria. With quantitative measurements especially, the definiteness of the result suggests, often misleadingly, a precision and simplicity in the outlines of the object measured. Measurements of national income are subject to this type of illusion and resulting abuse, especially since they deal with matters that are the center of conflict of opposing social groups where the effectiveness of an argument is often contingent upon oversimplification.


In short, GDP is an oversimplification, and as such it cannot capture something as complex as an economy, or the multifaceted needs of a society. In our paper, we focus on some of its specific aspects. Income inequality skews the richness distribution, so that GDP doesn’t describe how the majority of the population is doing. But more importantly, it is not possible to quantify well-being just with the number of dollars in someone’s pocket: she might have dreams, aspirations and sophisticated needs that bear little to no correlation with the status of her wallet. And even if GDP was a good measure, it’s very hard to calculate: it takes months to estimate it reliably. Nowcasting it would be great.

And so we tried to hack our way out of GDP. The measure we decided to use is the one of customer sophistication, that I presented several times in the past. In practice, the measure is a summary of the connectivity of a node in a bipartite network*. The bipartite network connects customers to the products they buy. The more variegated the set of products a customer buys, the more complex she is. Our idea was to create an aggregated version at the network level, and to see if this version was telling us something insightful. We could make a direct correlation with the national GDP of Italy, because the data we used to calculate it comes from around a half million customers from several Italian regions, which are representative of the country as a whole.


The argument we made goes as follows. GDP stinks, but it is not 100% bad, otherwise nobody would use it. Our sophistication is better, because it is connected to the average degree with which a person can satisfy her needs**. Income inequality does not affect it either, at least not in trivial ways as it does it with GDP. Therefore, if sophistication correlates with GDP it is a good measure of well-being: it captures part of GDP and adds something to it. Finally, if the correlation happens with some anticipated temporal shift it is even better, because GDP pundits can just use it as instantaneous nowcasting of GDP.

We were pleased when our expectations met reality. We tested several versions of the measure at several temporal shifts — both anticipating and following the GDP estimate released by the Italian National Statistic Institute (ISTAT). When we applied the statistical correction to control for the multiple hypothesis testing, the only surviving significant and robust estimate was our customer sophistication measure calculated with a temporal shift of -2, i.e. two quarters before the corresponding GDP estimate was released. Before popping our champagne bottles, let me write an open letter to the elephant in the room.


As you see from the above chart, there are some wild seasonal fluctuations. This is rather obvious, but controlling for them is not easy. There is a standard approach — the X-13-Arima method — which is more complicated than simply averaging out the fluctuations. It takes into account a parameter tuning procedure including information we simply do not have for our measure, besides requiring observation windows longer than what we have (2007-2014). It is well possible that our result could disappear. It is also possible that the way we calculated our sophistication index makes no sense economically: I am not an economist and I do not pretend for a moment that I can tell them how to do their job.

What we humbly report is a blip on the radar. It is that kind of thing that makes you think “Uh, that’s interesting, I wonder what it means”. I would like someone with a more solid skill set in economics to take a look at this sophistication measure and to do a proper stress-test with it. I’m completely fine with her coming back to tell me I’m a moron. But that’s the risk of doing research and to try out new things. I just think that it would be a waste not to give this promising insight a chance to shine.

* Even if hereafter I talk only about the final measure, it is important to remark that it is by no means a complete substitute of the analysis of the bipartite network. Meaning that I’m not simply advocating to substitute a number (GDP) for another (sophistication), rather to replace GDP with a fully-blown network analysis.

** Note that this is a revealed measure of sophistication as inferred by the products actually bought and postulating that each product satisfies one or a part of a “need”. If you feel that the quality of your life depends on you being able to bathe in the milk of a virgin unicorn, the measure will not take into account the misery of this tacit disappointment. Such are the perils of data mining.

16 October 2015 ~ 0 Comments

Central Places and Sophistication


Looking at a population map, one may wonder why sometimes you find metropoles in the middle of nowhere — I’m looking at you, Phoenix. Or why cities are distributed the way they are. When in doubt, you should always refer to your favorite geographer. She would probably be very happy to direct your interest to the Central Place Theory (CPT), developed by Walter Christaller in the 30s. The theory simply states that cities provide services to the surrounding areas. As a consequence, the big cities will provide many services and small cities a few, therefore the small cities will gravitate around larger settlements. This smells like complexity science to me and this post is exactly about connecting CPT with my research on retail customer sophistication and mobility. But first I need to convince you that CPT actually needs this treatment.

CPT explains why sometimes you will need a big settlement in the middle of the desert. That is because, for most of history, civilizations relied on horses instead of the interwebz for communication and, with very long stretches of nothing, that system would fall apart. That is why Phoenix has been an obsolete city since 1994 at the very least, and people should just give it up and move on. You now might be tempted to take a look at the Wikipedia page of the Central Place Theory to get some more details. If you do, you might notice a few “simplifications” used by Christaller when developing the theory. And if you don’t, let me spoil it for you. Lo and behold, to make CPT work we need:

  • An infinite flat Earth — easy-peasy-lemon-squeezy compared to what comes next;
  • Perfectly homogeneous distribution of people and resources;
  • Perfectly equidistant cities in a grid much like the one of Civilization 5;
  • The legendary perfect competition and rational market conjured by economists out of thin air;
  • Only one mode of transportation;
  • A completely homogeneous population, all equal in desires and income.

In short, the original CPT works in a world that is no more real than Mordor.


And here where’s sophistication comes into play. I teamed up with Diego Pennacchioli and Fosca Giannotti with the objective of discovering the relationship between CPT and our previous research on sophistication — the result is in the paper Product Assortment and Customer Mobility, just published on EPJ Data Science. In the past, we showed that the more sophisticated the needs of a customer, the further the customer is willing to travel to satisfy those. And our sophistication measure worked better than other product characteristics, such as the price and its average selling volume.

Now, to be honest, geographers did not sleep for 80 years, and they already pointed out the problems of CPT. Some of them developed extensions to get rid of many troubling assumptions, others tested the predictions of these models, others just looked at Phoenix in baffled awe. However, without going too in depth (I’m not exactly qualified to do it) these new contributions are either very theoretical in nature, or they haven’t used larger and more detailed data validation. Also, the way central places are defined is unsatisfactory to me. Central places are either just very populous cities, or cities with a high variety of services. For a person like me trained in complexity science, this is just too simple. I need to bring sophistication into the mix.


Focusing on my supermarket data, variety is the number of different products provided. Two supermarkets selling three items have the same variety. Sophistication requires the products not only to be different, but also to satisfy different needs. Suppose shop #1 sells water, juice and soda, and shop #2 sells water, bread and T-shirts. Even if the shops have the same variety, one is more sophisticated than the other. And indeed the sophistication of a shop explains better the “retention rate” of a shop, its ability to preserve its customer base even for customers who live far away from the shop. That is what the above table reports: controlling for distance (which causes a 2.6 percentage point loss of customer base per extra minute of travel), each standard deviation increase in sophistication strengthens the retention rate by 11 percentage points. Variety of products does not matter, the volume of the shop (its sheer size) matters just a bit.

In practice, what we found is that CPT holds in our data where big supermarkets play the role of big cities and provide more sophisticated “services”. This is a nice finding for two reasons. First, it confirms the intuition of CPT in a real world scenario, making us a bit wiser about the world in which we live — and maybe avoiding mistakes in the future, such as creating a new Phoenix. This is non-trivial: the space in our data is not infinite, homogeneous, with a perfect market and it has differentiated people. Yet, CPT holds, using our sophistication measure as driving factor. Second, it validates our sophistication measure in a theoretical framework, potentially giving it the power to be used more widely than what we have done so far. However, both contributions are rather theoretical. I’m a man of deeds, so I asked myself: are there immediate applications of this finding?


There might be one, with caveats. Remember we are analyzing hundreds of supermarkets in Italy. We know things about these supermarkets. First, we have a shop type, which by accident correlates with sophistication very well. Then, we know if the shop was closed down during the multi-year observation period. We can’t know the reason, thus everything that follows is a speculation to be confirmed, but we can play with this. We can compare the above mentioned retention rate of closing and non-closing shops. We can also define a catch rate. While “retention” meant how many of your closest customers you can keep, catch means how many of the non-closest customers you can get. The above plots show retention and catch ratios. The higher the number the more the ratio is in favor of the non-closing shop.

For the retention rate, the average sophistication shops (green) have by far the largest spread between shops that are still open and the ones which got shut down. It means that these medium shops survive if they can keep their nearby customers. For the catch rate, the very sophisticated shops (red) are always on top, regardless of distance. It means that large shops survive if they really can attract customers, even if they are not the closest shop. The small shops (blue) seem to obey neither logic. The application of this finding is now evident: sophistication can enlighten us as to the destiny of different types of shops. If medium shops fail to retain their nearby customers, they’re likely to shut down. If large shops don’t catch a wider range of customers, they will shut down. This result talks about supermarkets, but there are likely connections with settlements too, replacing products with various services. Once we calculate a service sophistication, we could know which centers are aptly placed and which ones are not and should be closed down. I know one for sure even without running regressions: Phoenix.


12 August 2015 ~ 1 Comment

Entropy Applied to Shopping

I don’t know about you guys, but when it comes to groceries I show behaviors that are strongly reminiscent of Rain Man. I go to the supermarket the same day of the week (Saturday) at the same time (9 AM), I want to go through the shelves in the very same order (the good ol’ veggie-cookies-pasta-meat-cat food track), I buy mostly the same things every week. Some supermarkets periodically re-order their shelves, for reasons that are unknown to me. That’s enraging, because it breaks my pattern. The mahātmā said it best:


Amen to that. As a consequence, I signed up immediately when my friends Riccardo Guidotti and Diego Pennacchioli told me about a paper they were writing about studying the regularity of customer behavior. Our question was: what is the relationship between the regularity of a customer’s behavior and her profitability for a shop? The results are published in the paper “Behavioral Entropy and Profitability in Retail“, which will be presented in the International Conference on Data Science and Advanced Analytics, in October. To my extreme satisfaction the answer is that the more regular customers are also the most profitable. I hope that this cry for predictability will reach at least the ears of the supermarket managers where I shop. Ok, so: how did we get to this conclusion?

First, we need to measure regularity in a reasonable way. We propose two ways. First, a customer is regular if she buys mostly the same stuff every time she shops, or at least her baskets can be described with few typical “basket templates”. Second, a customer is regular if she shows up always at the same supermarket, at the same time, on the same day of the week. We didn’t have to reinvent the wheel to figure out a way for evaluating regularity in signals: giants of the past solved this problem for us. We decided to use the tools of information theory, in particular the concept of information entropy. Information entropy tells how much information there is in an event. In general, the more uncertain or random the event is, the more information it will contain.


If a person always buys the same thing, no matter how many times she shops, we can fully describe her purchases with a single bit of information: the thing she buys. Thus, there is little information in her observed shopping events, and she has low entropy. This we call Basket Revealed Entropy. Low basket entropy, high regularity. Same reasoning if she always goes to the same shop, and we call this measure Spatio-Temporal Revealed Entropy. Now the question is: what does happen to a customer’s expenditure for different levels of basket and spatio-temporal entropy?

To wrap our heads around these two concepts we started by classifying customers according to their basket and spatio-temporal entropy. We used the k-Means algorithm, which simply tries to find “clumps” in the data. You can think of customers as ants choosing to sit in a point in space. The coordinates of this point are the basket and spatio-temporal entropy. k-Means will find the parts of this space where there are many ants nearby each other. In our case, it found five groups:

  1. The average people, with medium basket and spatio-temporal entropy;
  2. The crazy people, with unpredictable behavior (high basket and spatio-temporal entropy);
  3. The movers, with medium basket entropy, but high spatio-temporal entropy (they shop in unpredictable shops at unpredictable times);
  4. The nomads, similar to the movers, with low basket entropy but high spatio-temporal entropy;
  5. The regulars, with low basket and spatio-temporal entropy.

Click to enlarge

Once you cubbyholed your customers, you can start doing some simple statistics. For instance: we found out that the class E regulars spend more per capita over the year (4,083 Euros) than the class B crazy ones (2,509 Euros, see the histogram above). The regulars also visit the shop more often: 163 times a year. This is nice, but one wonders: why haven’t the supermarket managers figured it out yet? Well, they may have been, but there is also a catch: incurable creatures of habit like me aren’t a common breed. In fact, if we redo the same histograms looking at the group total yearly values of expenditures and baskets, we see that class E is the least profitable, because fewer people are very regular (only 6.9%):

Click to enlarge

Without dividing customers in discrete classes, we can see what is the direct relationship between behavioral entropy and the yearly expenditure of a customer. This aggregated behavioral entropy measure is simply the multiplication of basket and spatio-temporal entropy. Unsurprisingly, entropy and expenditure are negatively correlated:


Finally, we want to quantify this relationship. We want to have an objective way to tell how much more money the supermarket could make if the customers would be more regular. We didn’t get too fancy here, just a linear model where we try to predict the customers’ expenditures from their basket and spatio-temporal entropy. We don’t care very much about causation here, we just want to make the point that basket and spatio-temporal entropy are interesting measures.

Click to enlarge

The negative sign isn’t a surprise: the more chaotic a customer’s life, the lower her expenditures. What the coefficients tell us is that we expect the least chaotic (0) customer to spend almost four times as much as the most chaotic (1) customer*. You can understand why this was an extremely pleasant finding for me. This week, I’m going to print out the paper and ask to see the supermarket manager. I’ll tell him: “Hey, if you stop moving stuff around and you encourage your customers to be more and more regular, maybe you could increase your revenues”. Only that I won’t do it, because that’d break my Saturday shopping routine. Oh dear.

* The interpretation of coefficients in regressions are a bit tricky, especially when transforming your variables with logs. Here, I just jump straight to the conclusion. See here for the full explanation, if you don’t believe me.