Andrea Baronchelli Talk

In early september, we have another great visitor, Andrea Baronchelli, who’s a faculty member at City University London. Andrea is a super-exciting young scientist with varied interests across complex systems science, and a collaborator on my recent Sapere Aude grant on influence in social systems. He will talk about a set of very cool experiment on the emergence of social conventions. Details below:
  • Date: September 2nd, 2015
  • Time: 11am
  • Location: Technical University of Denmark, Building 321, 1st floor lab space
  • Title: The Spontaneous Emergence of Social Conventions: An Experimental Study of Cultural Evolution
  • Abstract: How do shared conventions emerge in complex decentralised social systems? This question engages fields as diverse as linguistics, sociology, and cognitive science. Previous empirical attempts to solve this puzzle all presuppose that formal or informal institutions, such as incentives for global agreement, coordinated leadership, or aggregated information about the population, are needed to facilitate a solution. Evolutionary theories of social conventions, by contrast, hypothesise that such institutions are not necessary in order for social conventions to form. However, empirical tests of this hypothesis have been hindered by the difficulties of evaluating the real-time creation of new collective behaviours in large decentralised populations. Here, I will present experimental results—replicated at several scales—that demonstrate the spontaneous creation of universally adopted social conventions and show how simple changes in a population’s network structure can direct the dynamics of norm formation, driving human populations with no ambition for large scale coordination to rapidly evolve shared social conventions. I will also show that a simple model describes well the experimental results on different classes of social networks.
References:
Experiment: D. Centola and A. Baronchelli, PNAS 112, 1989 (2015)
Model: A. Baronchelli, M. Felici, V. Loreto, E. Caglioti and L. Steels, JSTAT P06014 (2006) (see also http://bit.ly/1U3L7YF )

Jim Bagrow Visit and Talk

This week my good friend & collaborator James Bagrow (assistant professor at University of Vermont) is visiting the group. He’s an excellent speaker, and we’re lucky enough that he’s agreed to give a talk as part of his visit. If you’re anywhere near Copenhagen, his talk is worth the trip out to DTU. Here are the details:

  • Time: Friday June 19th, 2015. 10:00am
  • Location: Technical University of Denmark, Building 321. First floor “Lab Space”. If you need directions, click here.
  • Title: Data-driven approaches to studying human dynamics
  • Abstract: Research on human dynamics and computational social science has been revolutionized by new data taken from online social networks. These modern datasets capture activity patterns across very large populations. Using these records, new results have been discovered and existing hypotheses have been tested. But what is the fundamental limit of social information stored in these data? These data also have sampling biases and other issues that make uncertainty quantification crucial. Along these lines, I will discuss current projects related to inferring hidden structure in partially observed networks and using large-scale Twitter data to estimate how information is stored and flows through social networks.  

(And Vedran Sekara’s PhD defense is that same afternoon).

Tracking Human Mobility using WiFi signals

When I started working on understanding social systems, privacy really wasn’t on my mind. (I generally want to write down equations, understand the universe and all that). But one of the central realizations arising from our SensibleDTU experiment is that privacy needs to be an important part of this kind of research. I’ve written about this at length elsewhere. One of the things we noticed while digging into terabytes of social data is that data-channels are highly correlated. Information “bleeds through” … something which has serious implications for privacy. Case in point: My group has just released a new preprint (get it here) that shows how the WiFi information routinely collected by your smartphone can easily be converted to precise information about your location. WiFi routers reveal where you live, work, and spend your leisure time. While your phone may have told you that WiFi helps “improve location accuracy”, it may come as a surprise that

  • A majority of apps in the store have access to the list of routers around you (scanned every 20 seconds).
  • Your Android smartphone by default scans for WiFi routers even if you disable WiFi.

Our research shows

  • How to easily convert WiFi information into geographical position.
  • That although it sounds like all WiFi scans might be a lot of data to process, your mobility can be described using just a few of access points. And we have built an Android app which only requires WiFi data to illustrate how this works for your own mobility: Download here.
  • That if someone knows these routers at some point in time, they will still know a lot about your mobility six months later.

Thus, while WiFi networks are intended for enabling connectivity, they are also a de facto location tracking infrastructure. More generally, our world is becoming more enclosed in a web infrastructures supporting communication, mobility, payments, and advertising. Logs from mobile phone networks (call detail records, CDRs) constitute a global database of human mobility and communication networks. Credit card records form high-resolution traces of our spending behaviors.

The figure shows 48 hours of location data of one of the authors, with the four visited locations visited marked in blue: home, two offices, and a food market. Even though the author's phone has sensed 3,822 unique routers in this period, only a few are enough to describe the location more than 90% of time. (a) traces recorded with GPS; (b) traces reconstructed using all available data on WiFi routers locations - the transition traces are distorted, but all stop locations are visible and the location is known 97% of the time. (c) with 8 top routers it is still possible to discover stop locations in which the author spent 95% of the time. In this scenario transitions are lost. (d) timeseries showing when during 48 hours each of the top routers were seen. It can be assumed that AP 1 is home, as it's seen every night, while AP 2 and AP 3 are offices, as they are seen during working hours. The last row shows the combined 95% of time coverage provided by the top 8 routers.
The figure shows 48 hours of location data of one of the authors, with the four visited locations visited marked in blue: home, two offices, and a food market. Even though the author’s phone has sensed 3,822 unique routers in this period, only a few are enough to describe the location more than 90% of time. (a) traces recorded with GPS; (b) traces reconstructed using all available data on WiFi routers locations – the transition traces are distorted, but all stop locations are visible and the location is known 97% of the time. (c) with 8 top routers it is still possible to discover stop locations in which the author spent 95% of the time. In this scenario transitions are lost. (d) timeseries showing when during 48 hours each of the top routers were seen. It can be assumed that AP 1 is home, as it’s seen every night, while AP 2 and AP 3 are offices, as they are seen during working hours. The last row shows the combined 95% of time coverage provided by the top 8 routers.

It is already a well know fact, that the so-called “WiFi scanners” can be used to track individuals. This is done by cities, airports, shopping centers, and advertisers (and perhaps intelligence agencies). Some OS manufacturers (e.g. Apple and Chainfire) have recently responded to such tracking by frequently randomizing the unique identifier of each phone. Randomizing the phone identifier, however, does not address the threat presented in our work—where data is collected by an application on the phone, not by external devices. The privacy of WiFi scan results is often overlooked. In the Android ecosystem the WiFi scans are not considered as a location signal. WiFi information can be collected by applications without location permission, do not show up in the overview of applications using location data, and the WiFi permission is not considered sensitive. This makes it possible for 3rd party developers to collect high-resolution mobility data under the radar, circumventing the policy and the privacy model of the Android ecosystem. Any app with just the WiFi permission can track your position, although they don’t necessary do (there are legitimate reasons for applications to ask for WiFi permission, although this permission seems to be requested more often than required). Last time we checked (February 2015), 17 out of 20 top games on Android Play Store required access to your WiFi data; in only 6 of those 17 cases their privacy policy provided reasons why this information is required. For more information, email the paper’s first author Piotr (pisa@dtu.dk), who collaborated on this post. Or me (sljo@dtu.dk). The preprint is available on arXiv.

Update June 3rd, 2015 (maybe-our-paper-played-a-role-in-this edition)

Yesterday, while scouring Google I/O for details on the updated permissions (and to see if anyone mentioned our work), we found that a Google engineer (Ben Poiesz) was asked about the issue of WiFi tracking during the session discussing the new permission model. The session took place on May 29th – the clip is here:

In the video, the friendly Google engineer notes that that – under the new system – apps without the location permission will no longer be able to see the mac addresses of WiFi and Bluetooth devices around … because that’s that’s equivalent to location.

No one is claiming (least of all us) that our work caused the change, but we would like to point out a couple of things about the way Google chose to announce it, which might indicate that the choice of fixing wifi is a recent decision on Google’s part:

  • The published source code [find it here] (lines 99-114) and documentation [find it here] do not yet indicate that WiFi information is to be treated as location.
  • When you install the current Android M beta on your phone, our “WiFi Watchdog”app still works … and WiFi is not treated as location. And a technical point: This it’s not just because of the “legacy mode” – according to the same presentation (https://youtu.be/f17qe9vZ8RM?t=13m): “WiFi Watchdog” should just receive empty data on Android M, but instead it continues to receive the same data as on Lollipop
  • The announcement of this arguably major change (80% of apps on the market would potentially be affected) was not a part of the main presentation … but an answer during the Q&A session.

Now, it is probably just a coincidence, and maybe a fix for the WiFi permissions has been in the works for months. But it’s quite striking that Google decided to fix wifi permissions 7 years after the existing scheme was introduced (and just days after we published our paper).

What it means to be a pro

I just love this quote which uses a Tiger Woods anecdote to illustrate what it means to be a professional. It’s from The War of Art by Steven Pressfield (a great read, btw).

With four holes to go on the final day of the 2001 Masters (which Tiger went on to win, completing the all-four-majors-at-one-time Slam), some chucklehead in the gallery snapped a camera shutter at the top of Tiger’s backswing. Incredibly, Tiger was able to pull up in mid-swing and back off the shot. But that wasn’t the amazing part. After looking daggers at the malefactor, Tiger recomposed himself, stepped back to the ball, and striped it 310 down the middle.
That’s a professional. It is tough-mindedness at a level most of us can’t comprehend, let alone emulate. But let’s look more closely at what Tiger did, or rather what he didn’t do.
First, he didn’t react reflexively. He didn’t allow an act that by all rights should have provoked an automatic response of rage to actually produce that rage. He controlled his reaction. He governed his emotion.
Second, he didn’t take it personally. He could have perceived this shutterbug’s act as a deliberate blow aimed at him individually, with the intention of throwing him off his shot. He could have reacted with outrage or indignation or cast himself as a victim. He didn’t.
Third, he didn’t take it as a sign of heaven’s malevolence. He could have experienced this bolt as the malice of the golfing gods, like a bad hop in baseball or a linesman’s miscall in tennis. He could have groaned or sulked or surrendered mentally to this injustice, this interference, and used it as an excuse to fail. He didn’t.
What he did do was maintain his sovereignty over the moment. He understood that, no matter what blow had befallen him from an outside agency, he himself still had his job to do, the shot he needed to hit right here, right now. And he knew that it remained within his power to produce that shot. Nothing stood in his way except whatever emotional upset he himself chose to hold on to.
That’s something to aspire to.

Visitors this month

This month we have a two excellent of long-term visitors in the group.

Visiting all month is Ivan Brugere a graduate from Tanya Berger-Wolff‘s group at University of Illinois, Chicago. Ivan is interested in Spatiotemporal network mining, Network inference and prediction, and Social network privacy modeling.

Stopping by between April 12th and April 18th is Laura Allesandretti, who’s a graduate student with Andrea Baronchelli at City University London. Laura, Andrea and I are studying the long-term changes in individual and collective mobility patterns. In the literature, human mobility is typically described on a meta-stable time-scale, where mobility is characterized by regular patterns. We are interested in how this meta-stable regime evolves over long stretches of time (years).

overal_network

Ivan & Laura will both be giving talks during their visits, so stay tuned for more info.

Petter Holme

Emphasizing our focus on temporal networks, I am happy to announce that temporal networks czar,  Petter Holme will visit the lab on Feb 18th. Petter is the author (with Jari Saramäki, who visited last week) of the recent  & excellent review on temporal networks.

He will be giving giving an talk, and if you’re in the neighborhood, I highly recommend attending.

  • Speaker: Petter Holme. Associate Professor. Department of Energy Science. Sungkyunkwan University, Suwon Korea
  • Date. Feburary 18th, 2015
  • Time. 14:00
  • Location. DTU, Building 321, room 134
  • Title: Temporal networks of human interaction

Abstract: Since the turn of the millennium, networks have become a universal paradigm for simplifying large-scale complex systems, and for studying their system-wide functionalities. At the same time, there is considerable evidence that temporal structures, such as the burst-like behavior of human activity, affect dynamic systems on the network. These two lines of research come together in the study of temporal networks. Over the last five years, there has been a growing interest in how to analyze and model datasets in which we not only know which units interact (like in a traditional, static network), but also when the interactions take place. Just like static network analysis, the development of temporal network theory has been accelerated by the availability of new datasets. It should be noted that temporal networks are more than just extensions of static networks—they are e.g. (unlike simple, directed, weighted and multiplex networks) not transitive. In other words, if A and B are connected, and B and C are also connected, this does not imply that A and C are connected. Perhaps for this reason, temporal network theory has focused less on structural measures and studies of simple evolutionary models, and more on randomization studies and the simulation of spreading on empirical data. I will describe the state of the field, my own contributions (mostly about how temporal contact patterns affect infectious disease spreading), and discuss some future challenges.