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 really interdisciplinary team of about 20 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. 

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Book on Interconnected Networks

We are happy to anounce that the book "Interconnected Networks" which was edited by Antonios Garas and published by Springer is now available. The book contains selected contributions and provides, both, an overview and an introduction to the emerging field of interconnected multilayered networks.

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The Network of Counterparty Risk: Analysing Correlations in OTC Derivatives

The Over-the-Counter derivatives market is one of the largest. It is, however, not as transparent as other, exchange traded markets. In our new article we attempt to reconstruct the network of OTC derivatives trade between major US banks, in which we observe a clear core-periphery structure over a long period of time.

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Online Privacy as a Collective Phenomenon

How much of our private information do our friends disclose about us, and how much of our privacy is lost simply because of online social interaction? In this paper we analyse how much a social network can discover about anyone whether they are a member of the social network or not.

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Paper published in Proceedings of WSDM 2014

By combining multiple social media datasets, it is possible to gain insight into each dataset that goes beyond what could be obtained with either individually. In this paper we combine user-centric data from Twitter with video-centric data from YouTube to build a rich picture of who watches and shares what on YouTube.

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Postdoc Position (data-driven modeling)

We welcome applications for an vacant postdoc position in the context of data-driven modeling. We offer excellent working conditions in a lively interdisciplinary team as well as a competitive salary.

More information on this position and how to apply is available here.

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How does the productivity of teams scale with size?

We are happy to announce that our latest article "From Aristotle to Ringelmann: a large-scale analysis of team productivity and coordination in Open Source Software projects" has now 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.

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Temporal Network Analysis in Python

Today, 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.

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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:

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IEEE SASO: Call for Papers available

In September 2015, the IEEE International Conference on Self-Adaptive and Self-Organizing Systems (IEEE SASO) will return to MIT in Boston, MA. Our group member Ingo Scholtes is involved in the Steering Comitee of this conference, which addresses the engineering of self-organizing, complex systems. A Call for Papers is now available online.

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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.

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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.

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Multinet.js: Graph visualizer in the browser

We are happy to introduce Multinet.js,  a visualization framework for large, dynamic and multi-layered graphs developed by the Chair of Systems Design. 

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"Dissonance minimization" in most read papers of 2014

We proudly present that our paper "Dissonance minimization as a microfoundation of social influence in models of opinion formation" was highlighted as one of the most downloaded papers of 2014 in the Journal of Mathematical Sociology.

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Paper published in Nature Communications

Our paper "Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks" has just been published in the journal Nature Communications. In this work, we study the influence of order correlations on causality structures in time-stamped networked data. Our results highlight an important additional dimension of complexity in temporal networks, and provide interesting perspectives for novel network-based ranking and clustering algorithms.

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