Welcome to the Chair of Systems Design

Our research can be best described as data driven modeling of complex systems, with particular emphasis on social, socio-technical, and socio-economic systems. We are a trully 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. 


How firms select their partners for R&D collaborations?

In our new preprint, we perform a large scale analysis of R&D networks using a data driven modeling approach. We monitor the selection of partners for R&D collaborations of firms both empirically, by analyzing a large data set of R&D alliances over 25 years, and theoretically, by utilizing an agent-based model of alliance formation. Using the weighted k-core decomposition method we derive a centrality-based career path for each firm, and analyzing coreness differences between firms and their partners, we identify a change in the way firms select partners.

We use the agent-based model to test whether this change in behavior can be attributed to strategic considerations, and we find that the observed behavior can be well reproduced without such considerations. This way we challenge the role of strategies in explaining macro patterns of collaborations.


Second symposium on Computational Social Science

On 27th January 2017 the second symposium on Computational Social Science will take place.
The symposium features three showcases of successful research in this area.

More information on the symposium and the program are available here.


ETH48 Workshop on Cascade Processes

As part of the ETH48 Research Project, the ETH Risk Center organizes a workshop that brings together experts on cascade phenomena from various disciplines. In particular, we study economic and financial systems, shed light on epidemic spreading, and look at similarities with information cascades in social online media.

The workshop will be held at 19 and 20 January 2017. You are very welcome to join. Please register via e-mail to Rebekka Burkholz (rburkholz(at)ethz.ch) until 16 January 2017.


«Decision-Making in Complex Environments: From Humans to Machines»

The 5th Risk Center Dialogue Event with focus on "Decision-Making in Complex Environments: From Humans to Machines" will be held on 20 January 2017.
The Dialogue Events are one of the major outreach activities of the Risk Center. These event especially foster the dialogue between the Risk Center Professors and the public.

More information on the workshop and the full programme are available here.
If you would like to participate, please register here.



Mastering the Challenges of our Digital Society

The workshop "Mastering the Challenges of our Digital Society" was held on 4th November 2016. This Risk Center workshop was organized by Prof. Frank Schweitzer and Dr. David Garcia together with Thomas Steiger from Zurich Insurance Company and addressed the challenges and opportunities of digitalization from the perspectives of three different stakeholders: citizens, companies and regulators.

More information on the workshop and the presentations of all speakers are available here.


When is a network a network?

Graph- and network-analytic methods are widely applied to data which capture relations between elements. Despite this popularity, we still lack principled methods to decide when network abstractions are justified and when not.

A new data mining framework developed at our chair can be used to answer the question when it is justified to make a network abstraction of sequential data on pathways and temporal networks. Building on principled model selection and statistical inference techniques, it further allows to infer optimal higher-order network models, which capture both temporal and toplogical characteristics of sequential data.

The methods proposed in this work have been implemented in the OpenSource python package pathpy, which is available on gitHub.


What we miss in network analysis

The analysis of relational data from a graph or network perspective has become a cornerstone of data mining. However, for data sets where additional information like, e.g. the timing or ordering of relations are available, in a number of recent works we have shown that the network perspective can yield wrong results. In our latest work published in the European Physical Journal B we now offer a solution, namely the analysis of higher-order networks. We specifically show that this promising abstraction allows us to (i) generalize common path-based centrality measures to higher-order centralities, and that (ii) these higher-order measures better capture the real importance of nodes in time-evolving network topologies.


How does the productivity of teams scale with size?

We are happy to announce that our article "From Aristotle to Ringelmann: a large-scale analysis of team productivity and coordination in Open Source Software projects" has been published in the journal Empirical Software Engineering. Using a data set of 58 OSS projects with more than 580,000 commits contributed by more than 30,000 developers, in this article we provide a large-scale analysis of the relation between size and productivity of software development teams.


Temporal Network Analysis in Python

We proudly present an in-depth educational tutorial showing how to analyze non-Markovian temporal networks using the python module pyTempNet which was recently developed at our chair. The tutorial features an interactive and hands-on introduction to our latest theoretical works on the analysis of time-stamped relational data. It demonstrates how our methods can be used to analyze the effect of order correlations in time-stamped network data, specifically showing how to analyze, simulate and visualize the effect of non-Markovian characteristics on dynamical processes.

The tutorial is available here.


Wikipedia research in the news

Our research in colaboration with GESIS Cologne has been featured in the news:

The results of our research are motivating editorial changes in Wikipedia:


Generalized Hypergeometric Ensembles

In this paper we introduce an ab initio class of statistical network ensembles based on a simple generative model of complex networks. We show that this class of ensembles provides a powerful framework for model selection in complex networks and a new approach to test the statistical signicance of community structures. The latest version of our paper"Generalized Hypergeometric Ensembles: Statistical Hypothesis Testing in Complex Networks" is available on ArXiv


How many developers does it take to complete a project?


Research covered by IEEE Software Blog


When Network Science Meets Software Engineering

On April 20, Ingo Scholtes will give an invited guest lecture in the special lecture series of the Elite Graduate Program in Software Engineering at Augsburg University, Germany. The lecture with the title "When Network Science meets Software Engineering" will introduce challenges and opportunities in the application of network-based data mining methods in the quantitative study of collaborative software engineering processes.


The Rise and Fall of R&D Networks

How are R&D networks structured? What are the driving forces behind the formation of inter-firm alliances? What are the reasons of the "rise and fall" trend exhibited by all industrial sectors in the last decades? These and more questions are answered in the latest version of our paper "The Rise and Fall of R&D Networks", now available on SSRN and ArXiv. Check also the nice visual example presented in the following video.