New paper in scientific reports

My work with Dijana Tolić and Nino Antulov-Fantulin “Simulating SIR processes on networks using weighted shortest paths” has been published in Scientific Reports:

Abstract: We present a framework to simulate SIR processes on networks using weighted shortest paths. Our framework maps the SIR dynamics to weights assigned to the edges of the network, which can be done for Markovian and non-Markovian processes alike. The weights represent the propagation time between the adjacent nodes for a particular realization. We simulate the dynamics by constructing an ensemble of such realizations, which can be done by using a Markov Chain Monte Carlo method or by direct sampling. The former provides a runtime advantage when realizations from all possible sources are computed as the weighted shortest paths can be re-calculated more efficiently. We apply our framework to three empirical networks and analyze the expected propagation time between all pairs of nodes. Furthermore, we have employed our framework to perform efficient source detection and to improve strategies for time-critical vaccination.


I will give a talk at the Controlling Complex Networks Symposium at NetSci

I am happy to announce that I will give a talk at the Controlling Complex Networks: When Control Theory Meets Network Science Satellite Symposium of NetSci2018 in the lovely city of Paris. The Symposium will take place June 11, 2018 (8:30am–6:05pm).

For more information, please visit

New paper in Nature Communications

I am happy to announce that my work “Metric clusters in evolutionary games on scale-free networks” was published in Nature Communications today.

The paper discusses the geometric organization of cooperators in evolutionary games on scale-free networks and reveals that heterogeneity does not always favor but can even hinder cooperation, which is the case when more high degree nodes break up the geometric structures.

Read the paper at

The evolution of cooperation in social dilemmas in structured populations has been studied extensively in recent years. Whereas many theoretical studies have found that a heterogeneous network of contacts favors cooperation, the impact of spatial effects in scale-free networks is still not well understood. In addition to being heterogeneous, real contact networks exhibit a high mean local clustering coefficient, which implies the existence of an underlying metric space. Here we show that evolutionary dynamics in scale-free networks self-organize into spatial patterns in the underlying metric space. The resulting metric clusters of cooperators are able to survive in social dilemmas as their spatial organization shields them from surrounding defectors, similar to spatial selection in Euclidean space. We show that under certain conditions these metric clusters are more efficient than the most connected nodes at sustaining cooperation and that heterogeneity does not always favor—but can even hinder—cooperation in social dilemmas.



Update: Failure of incentives in multiplex networks

Update of our preprint now with a new name! Check it out at

Title: Failure of incentives in multiplex networks

Abstract: Governments and enterprises strongly rely on incentives to generate favorable outcomes from social and strategic interactions between individuals, for example climate or environmental friendly actions. The incentives are usually modeled by payoffs in strategical games, such as the prisoner’s dilemma or the harmony game. Adjusting the incentives by changing the payoff parameters e.g. through tax schemes can favor cooperation (harmony) over defection (prisoner’s dilemma). Here, we show that if individuals engage in strategic interactions in multiple domains, incentives can fail and the final outcome, cooperation or defection, is dominated by the initial state of the system. Our findings highlight the importance to take the multilayer structure of human interactions into account and emphasize the importance to rethink payoff-based incentives.