Later this month, we’re lucky to have Jari Saramäki visiting and speaking at the lab. Jari is an expert on temporal networks (I highly recommend the excellent review paper on temporal networks that Jari co-authored with past and future guest of the lab, Petter Holme).

Jari is an associate professor at Aalto University and a highly cited author of many high impact papers on complex networks, for example:

  • Jari Saramäki, E. A. Leicht, Eduardo López, Sam G. B. Roberts, Felix Reed-Tsochas, and Robin I. M. Dunbar. Persistence of social signatures in human communication. PNAS 111 (3) 942-947 (2014).
  • Lauri Kovanen, Kimmo Kaski, János Kertész, and Jari Saramäki. Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. PNAS 110 (45) 18070-18075 (2013).
  • J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, D. Lazer, K. Kaski, J. Kertész, and A.-L. Barabási. Structure and tie strengths in mobile communication networks. PNAS 104 (18) 7332-7336 (2007).

Below are the details of his talk.

From minutes to months: call network dynamics at multiple timescales

  • Date: 27th of January
  • Time: 14:00
  • Location: Auditorium 040 in DTU Building 324.

Abstract: Big Data on human interactions and communication have revolutionized the ways how human behaviour can be approached from a quantitative point of view. Mobile telephone Call Detail Records (CDR’s) have proven especially fruitful for understanding one-to-one communication patterns and the dynamics of inferred social networks. I will discuss what happens and when in call networks constructed from CDR’s with time stamps; this talk can be considered a mini-review of what we know about temporal networks of mobile telephone calls. I will begin with short timescales and fast dynamics (such as burstiness of sequences of calls between individuals) and “zoom out”, from temporal motifs formed of correlated calls between multiple individuals to long-term dynamics of personal networks of individuals.

Privacy Part I: Why everyone is complaining, but no one is taking action.

We all have a sense that privacy is important. A sense that our ability to freely express “who we are” is slowly eroded by large corporations and governments collecting data on our actions for purposes not clear to us (and maybe not to them either). But on the other hand, no one is doing anything about this. Why is that?

I think that there are two central reasons for this.

The first reason is that humans are not very good at handling situations where cause and effect is separated by a lots of time and space. (I was made aware of this point by an excellent column in the Guardian by the author Cory Doctorow, who I will be stealing from in the following). There are lots of examples of this: No one would smoke if you developed cancer immediately upon the first drag of a cigarette. The possibility of cancer is so far away in time that it feels like the consequences happen to another person. You would be less likely to  binge-eat if the food immediately was converted into belly flab.

Something similar is going on with privacy. You don’t immediately notice any problems when you hand over all of your email correspondence to Google or outsource your social network to Facebook, or signing up for free Airport Wifi. And it’s even worse because we don’t even know what the consequences of sharing those data might be. Figuring out what we can learn about individuals is an emerging field. And while we know that you can estimate e.g. your political views based on your Facebook feed, we’re still working out what it really is that you’re revealing about yourself, when you’re sharing data … and how those pieces of information can be used to manipulate you.

In fact, the entities that know most about what your personal data can be used for (e.g. Google, Facebook, Apple, Amazon), have very little incentive to share this information with the general public. In part because opening up can damage their public image (e.g. the Facebook mood experiment), and in part because sharing insights might run counter to shareholder interests (e.g. making tons of $$$ manipulating people) [1]. This is why the kind of research that we do here at DTU is so important – providing a public and open counterpoint to large corporation with private research divisions.

The second (and even more important) reason is that it is not at all clear what kind of action we should take. Privacy is such a complex issue that even if you want to take action, there is no obvious path to follow. To make such a path a little bit clearer is, my goal with the posts in this series. I’ll try to find a little bit of solid ground so that maybe we’ll have something to mobilize around once we feel like it’s time to take action.


[1] Thanks to Piotr Sapiezynski for making this point.

Some thoughts on privacy. Part 0

Recently I’ve been thinking a lot about privacy.

My own work focuses on what we can learn from dense data collected by volunteers at my university (DTU), and that means that privacy is something I think about a lot. What we’re learning from our amazing dataset shows that data channels are highly overlapping and even something like which WiFi access points your phone sees (sounds innocuous, right?) reveals pretty much everything about both your movement around in space as well as your social connections.

Because or our findings, I am concerned, for example, by the City of Copenhagen’s decision to monitor everyone in the city using WiFi routers. It’s not trivial to me that it is OK for the city to perform this kind of monitoring. More generally, the further we move into a future, where we haven’t set down simple ground rules for what’s OK and what is not OK, the more difficult it will be to find our way again.

Overall, however, I think sharing data is a great idea and usually data-sharing is a win-win proposition. But we have to make sure that we have rules that ensures the right balance of power between individuals and the entities that use their data.

Inspired by Clive Thompson’s thoughts on public thinking, I’ve decided to write a few posts about privacy and data on this blog even though I haven’t really figured out what to think about all aspects of the topic yet.

Thus, in the coming weeks (probably months, knowing my tendency to procrastinate), I’ll be writing about privacy here. A tentative outline of the series is:

  • Part I: Why everyone is complaining, but no one is taking action.
  • Part II: Some examples of why privacy is important.
  • Part III: Why technical solutions will not work.
  • Part IV: Suggestion for simple rules for data.
  • Part V: Sharing and electronic traces present an even deeper problem. I’ll present a sketch of a solution.
  • Part VI: Why all this does not mean you should not share your data. It’s generally a great idea to share with data both corporations and governments. Maybe also something about why companies with reasonable data policies will have a competitive advantage.

Talks next weeks

It’s not just the network structure that we care about. We want to understand network structure in order to get a handle on processes taking place on networks. That kind of processes is what next week’s two exciting (Monday and Tuesday October 6th and 7th, at 11am @ DTU) talks focus on. Both talks are open to the public, so I hope you’ll join us if you’re in Copenhagen. Full details here:

Cornelia Betsch on Vaccination Decision Making

  • Time: Monday, October 6th, 2014
  • Place: Technical University of Denmark, Building 321, 1st floor Lab Space
  • Title: Vaccination decision making – an individual and social perspective
  • Speaker: Dr. Cornelia Betsch. PD Dr. Cornelia Betsch is research fellow (Akademische Oberrätin) and scientific manager of the Center for Empirical Research in Economics and Behavioral Science (CEREB) at the University of Erfurt, Germany. She serves as a member of the European Technical Advisory Group of Experts on Immunization (ETAGE) of the WHO Europe and as a member of the German Commission for the Verification of Measles and Rubella Elimination(Federal Ministry of Health @ Robert Koch Institute).
  • Abstract: The desperate search for a vaccine against Ebola currently reminds us on the merits and value of vaccination. Still, there is a small but critical amount of parents and adults who decide not to vaccinate their children or themselves. They endanger public health goals such as the elimination of diseases like measles or polio. In this talk I will show from the individual perspective what may influence a decision against vaccination. Further, I will analyze the vaccination decision from a structural point of view and show the social perspective of vaccination decision making: as many vaccinated individuals can protect some unvaccinated individuals, it may be rational to forego vaccination and to free ride. Given we know something about how people make vaccination decisions, which strategies should we choose for vaccine advocacy? In the final part of the talk I will give some examples and link them to real-world challenges of vaccine communication.

Jens Koed on Describing the psychology of argumentation

  • Time: Tuesday, October 7th, 2014
  • Place: Technical University of Denmark, Building 321, 1st floor Lab Space
  • Title: Describing the psychology of argumentation, reasoning, and persuasion from a Bayesian perspective
  • Speaker: Jens Koed Madsen (Postdoc @ Birkbeck, University of London)
  • Abstract: Classical psychological models of persuasion and reasoning (Chaiken, 1980; Petty & Cacioppo, 1981) conceptualise rationality from the perspective of formal logical reasoning. Empirically, however, humans do not respond in line with logical predictions, as many fallacious arguments are accepted, and not all valid arguments are accepted. This has led to the conclusion that humans are not rational and to the development of the dual-process theory (consisting of a slow, laboured, and logical and a shallow, heuristic, and non-logical system). Recently, rationality has been recast as reasoning from uncertainty rather than reasoning from certainty from a Bayesian perspective (Oaksford & Chater, 2007). The paradigm has successfully been applied to reasoning (e.g. Oaksford & Chater, 2007), argumentation (e.g. Hahn & Oaksford, 2006; 2007), fallacies (e.g. Corner et al., 2011; Harris et al., 2012), persuasion (Madsen, 2013), and has integrated source credibility in a reasoning framework (Hahn et al., 2009; Harris et al., submitted). I work on three aspects of Bayesian persuasion: the conceptual development of the persuasion model from the thesis (Madsen, 2013), the psychological ontogenesis of probabilistic estimations, and the relationship between individualised approaches to belief changes and behaviour changes. These aspects touch upon the modelling, theoretical foundation, and application of the Bayesian approach developed in the past decade.

Bibliography for Jens’ talk

Chaiken, S. (1980) Heuristic versus systematic information processing and the use of source versus message cues in persuasion, Journal of Personality andSocial Psychology 39, 752-766

Corner, A., Hahn, U. & Oaksford, M. (2011). The psychological mechanism of the slippery slope argument. Journal of Memory & Language, 64, 133-152.

Hahn, U., Harris, A. J. L., & Corner, A. (2009). Argument content and argument source: An exploration. Informal Logic, 29, 337-367.

Hahn, U. & Oaksford, M. (2006a) A Bayesian Approach to Informal Reasoning Fallacies. Synthese 152, 207-23

Hahn, U., & Oaksford, M. (2007a) The rationality of informal argumentation: A Bayesian approach to reasoning fallacies, Psychological Review 114, 704-732

Hahn, U., Oaksford, M., & Harris, A. J. L. (2012). Testimony and argument: A Bayesian perspective. In F. Zenker (Ed.), Bayesian Argumentation (pp. 15-38). Dordrecht: Springer.

Harris, A. J. L., Hahn, U., Madsen, J. K. & Hsu, A. S. (submitted) The Appeal to Expert Opinion: Quantitative support for a Bayesian Network Approach, Cognitive Science, XXX, xxx-xxx

Madsen, J. K. (2013) Prolegomena to a Theory and Model of Persuasion Processing: A Subjective-Probabilistic Interactive Model of Persuasion (SPIMP), unpublished thesis, University College London

Oaksford, M. & Chater, N. (2007) Bayesian Rationality: The probabilistic approach to human reasoning. Oxford, UK: Oxford University Press.

Petty, R. E. & Cacioppo, J. T. (1981) Attitudes and persuasion: Classic and contemporary approaches, Boulder, CO: Westview Press

How to kill a Twitter Bot!

This friday we’re lucky to have visitor Emilio Ferrara presenting a talk on identifying twitter bots. Emilio’s work has been covered extensively in the media, for example MIT Technology Review’s How to spot a social bot on twitterDetails below:

  • Date: Friday September 12th, 2014
  • Time: 11:00-noon
  • Place: DTU Building 321, first floor lab space
  • Speaker: Emilio Ferrara (@jabawack), Post-doctoral Research Fellow at Indiana University Bloomington
  • Title: The rise of social bots: fighting deception and misinformation on social media
  • Abstract: One of the classic problems in Computer Science, recognizing the behavior of a human from that of a computer algorithm (proposed by Alan Turing), has suddenly become very relevant in the context of social media. Limits to the expressive power of humans and real incentives abound to develop human-mimicking software agents called social bots. These elusive entities wildly populate social media ecosystems, often going unnoticed among the population of real people. Bots can be harmful, aiming at persuading, smearing, or deceiving, and for such a reason our research aims at developing efficient systems to detect them. In my talk I will discuss the characteristics of modern, sophisticated social bots, and how their presence can endanger online ecosystems and our society. Characteristics related to content, network, sentiment, and temporal patterns of activity are imitated by bots but at the same time can help discriminate synthetic behaviors from human ones, yielding signatures of engineered social tampering. I will present “Bot or Not?”, a social bot detection framework prototype developed at Indiana University under the Truthy project. My talk will conclude depicting future scenarios and discussing related problems, such as that of studying persuasion campaigns on social media, how they spread, and how we can promptly detect and potentially hinder their diffusion.


Dynamic and Multiplex Networks

Network science buffs are in for a treat this Monday (September 1st, 2014), when we have a great set of visitors in my Group at DTU.  I’m excited to present talks on the cutting edge on what we know about networks from János Kertész and Janos Török. The talks will be back to back and detailed info can be found below

The talks are open to the public, so hope to see you there!

  • Time: Monday September 1st, 10am – noon
  • Place: DTU Building 321, room 134 (1st floor lab area).
  • Speakers:
    • János Török (10am-11pm). Associate professor at Budapest University of Technology, Department of Theoretical Physics.
    • János Kertész (11am-12pm). Professor & Director of the Institute of Physics, Budapest University of Technology and Economics

Multi-level, multi-channel, multi-agent modeling of social interactions (János Török)

Abstract: We present a model of society. Human relations are strengthened by communication and eroded by time. Communication is, in general, related to some social activity (work, friendship, hobby) or social context. Therefore we postulate that individuals having different social needs participate in a number of social contexts (family, workplace etc.) – which may also evolve in time – and communicate with other members of the contexts using different communication channels (face to face, phone, email, etc.) for different purposes and with different impact on their relationship. We show that using realistic input data from surveys and statistical data one can reproduce important features of real society like Dunbar’s numbers and their meaning.

Spreading on temporal networks: Results from empirical analysis, model calculations and simulation (János Kertész)

Abstract: Spreading phenomena typically take place on temporal networks, where connections between the nodes are only occasionally and for limited time present. Such events can be, e.g., encounters of people, which are important for contagion or opening a communication channel needed for information transmission. We studied a mobile call network from this point of view: Having the time stamped records of the calls we played a ‘susceptible-infected’ game by infecting one node at random and assuming transmission at every possible event. We introduced different reference systems by appropriate shuffling of the data and identified this way the contributions of the different types of correlations to the speed of spreading. We concluded that there is a considerable slowing down as compared to the random models, mainly due to the correlations between the link weights and the topology and the inhomogeneous, bursty character of the events. We have also shown that the temporal inhomogeneity cannot be characterized by the inter-event time distribution (IETD) alone as there are strong dependencies between the events. In order to understand better the role of the different components we investigated models of temporal networks. In the analytically solvable infinite complete graph we showed that burstiness, i.e., power law IETD distribution always accelerates the process provided the clocks are positioned on the nodes. For the complementary case of link related burstiness we considered a number of models, like the analytically tractable Cayley tree, BA trees and networks. We show that if the stationary bursty process is governed by power-law IETD, the spreading can be slowed down or accelerated as compared to a Poisson process; the speed is determined by the short time behavior, which in our model is controlled by the exponent. We demonstrate that finite, so called “locally tree-like” networks, like the Barabási-Albert networks behave very differently from real tree graphs if the IETD is strongly fat-tailed, as the lack or presence of rare alternative paths modifies the spreading. A further important result is that the non-stationarity of the dynamics has a significant effect on the spreading speed for strongly fat-tailed power-law IETDs, thus bursty processes characterized by small power-law exponents can cause slow spreading in the stationary state but also very rapid spreading heavily depending on the age of the processes.


1. M. Karsai, M. Kivelä, R. K. Pan, K. Kaski, J. Kertész, A.-L. Barabási, J. Saramäki: Small But Slow World: How Network Topology and Burstiness Slow Down Spreading, Phys. Rev. E 83, 025102 (2011)

2. Márton Karsai, Kimmo Kaski, Albert-László Barabási, János Kertész: Universal features of correlated bursty behavior, Scientific Reports 2, Article number 397 (2012)

3. Márton Karsai, Kimmo Kaski, János Kertész: Correlated dynamics in egocentric communication networks, PLoS ONE 7(7) e40612 (2012)

4. Hang-Hyun Jo, Márton Karsai, János Kertész, Kimmo Kaski: Circadian pattern and burstiness in human communication activity, New J. Phys. 14 013055 (2012)

5. Szabolcs Vajna, Bálint Tóth, János Kertész: Modelling power-law distributed interevent times, New J. Phys.15, article 103023 (2013)

6. Hang-Hyun Jo, Juan I. Perotti, Kimmo Kaski, János Kertész: Enhanced Spreading Dynamics by Non-Poissonian Processes, Physical Review X 4, 011041 (2014)

7. Dávid X. Horváth, János Kertész: Spreading dynamics on networks: the role of burstiness, topology and non-stationarity, New Journal of Physics 16 (7), 073037

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