Welcome to the Chair of Systems Design

Our research can be best described as data driven modelling of complex systems with particular emphasis on social, socio-technical, and socio-economic systems. We are a truly interdisciplinary team of about 15 people from various disciplines (statistical physics, applied mathematics, computer science, social science, engineering). And, yes, we do all the cool stuff, from big data analysis to multilayer network models, from social software engineering to predictions of scientific success - not to forget our research on polarization in political systems, cooperation in animal societies, and life cycles of R&D networks. Just click through our publications, funded projects, teaching or media coverage. 


Scientific Networks and Success

Every researcher is affected by how scientific performance is measured. How should it be measured? Do we have the right data to do it?

If you eager to have answers to these questions, come to our Satellite Workshop Scientific Networks and Success of CCS 2020.


Lead Agency Project granted

We are happy to announce that our Lead Agency Project "Signed Relations and Structural Balance in Complex Systems: From Data to Models" was granted by the SNF and NCN. We will carry out this project together with Prof. Holyst's group of Physics in Economy and Social Sciences at Warsaw University of Technology (Faculty of Physics).


Was ist ein Modell und wozu brauchen wir es?

Dazu ist am 12. März 2020 auf dem Blog des Verbands Digital Humanities im deutschsprachigen Raum ein Beitrag von Ramona Roller erschienen.
Den Blogpost finden Sie hier.


Resilience and performance of networked systems

Please find the program, abstracts and recorded talks at


International crop trade networks: The impact of shocks and cascades


Our most recent paper has been accepted for publication in Environmental Research Letters. To learn more, visit click the link.


New theory explains political polarization

Scientists at the CSH expand an old theory of balance to explain the emergence of hyperpolarization

A new model of opinion formation shows how the extent to which people like or dislike each other affects their political views —and vice versa. The resulting division of societies can even become a matter of life and death, as the current crises show.

A Weighted Balance Model of Opinion Hyperpolarization


Network Analysis in R

The latest version of our R library ghypernet is available on CRAN.

The library allows studying multi-edge networks using the framework of the generalised hypergeometric ensemble of random graphs. Install the package by running install.packages('ghypernet') in R, or go to ghyper.net for more information.


Who are the leaders and followers among bats?

Bats don't tell! But we find out.  See our two new manuscripts here: paper 1, paper 2



Improving the robustness of online social networks: A simulation approach of network interventions

Online social networks (OSN) are prime examples of socio-technical systems in which individuals interact via a technical platform. We study the emergence of large drop-out cascades of users leaving the OSN by means of an agent-based model. Our aim is to prevent such drop-out cascades by influencing specific agents. We identify strategies to control agents such that drop-out cascades are significantly reduced, and the robustness of the OSN is increased. Read more on arXiv.


Modeling User Reputation in Online Social Networks: The Role of Costs, Benefits, and Reciprocity

Intuitively, one would expect that higher costs lead to more users leaving an online social network (OSN) and hence decrease its robustness. We demonstrate that an optimal cost level exists, which maximizes both the performance of the OSN, measured by means of the long-term average benefit of its users, and the robustness of the OSN, measured by means of the life-time of the core of the OSN.Read more: http://arxiv.org/abs/1909.04591


Higher-order models capture changes in controllability of temporal networks


HONS2020 program is online

The program of our HONS satellite at NetSci 2020 is online at https://uzhdag.github.io/hons_web/. It will take place online on September 17, 2020.

HONS is the NetSci satellite for researchers that try to understand what we miss when we analyze graphs and network abstractions of complex systems. Its focus is on cutting-edge Higher-Order Network modelling techniques, which generalize network science techniques to models that account for higher-order dependencies in data on real systems.


GHYPERNET in the top 40!

The RStudio blog has listed our ghypernet R package in the top 40 picks of May 2020!

May 2020: "Top 40" New CRAN Packages


Let's spend the night together?

Bechstein's bats form groups of different size to spend the day together in several roosts. At dusk, these groups dissolve, at dawn they may re-merge. So, what is a typical group size? And how long does a group use the same roost? We answer these questions by analyzing empirical data from two colonies. What is more, we also provide an agent-based model to reproduce these findings. See our paper here.


The law of proportionate growth and its siblings

We combine the law of proportionate growth with additive growth terms, to develop an agent-based modeling framework with vast applications in social and economic systems. The paper discusses phenomena as diverse as saturated growth, competition, stochastic growth, investments in random environments, wealth redistribution, opinion dynamics and the wisdom of crowds, reputation dynamics, knowledge growth, and the combination with network dynamics. Read more on ArXiv