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.
We welcome applications for a doctoral position in Computational Social Science.
More information on this position and how to apply is available here.
How can we quantify the significance of links in relational data?
In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.
We're proud to see what former members of our chair achieved in industry. At the podium discussion about Data Science in Industry on Wednesday, 21st of November 2018, Dr. Antonios Garas, Dr. Pavlin Mavrodiev and Dr. Mario Tomasello, among others, talked about their everyday live while working in industry. They talked about the skills they brought to the job, the ones they acquired on the fly, and the ones that make them successful in reaching their goals.
In this paper, we propose an efficient and practical method to identify valid bug reports which a) refer to an actual software bug, b) are not duplicates and c) contain enough information to be processed right away.
How big is the risk that a few initial failures of nodes in a network amplify to large cascades? Predicting the final cascade size is critical to ensure the functioning of a system as a whole.
To make this prediction, we often compute the average cascade size using local tree approximations or mean field approximations. Yet, as we demonstrate in our recent work, in finite networks, this average does not even need to be a likely outcome. Instead, we find broad and even bimodal cascade size distributions.
We are proud that our KDD 2017 paper on the analysis of time-stamped and sequential network data has been covered by ETH News. Our work casts a critical light on the pervasive use of network analysis methods in various contexts, including infrastructure systems, information systems, and health. We provide a novel data mining framework that allows to overcome limitations of existing network-based techniques for time series data, improving our ability to model and analyze complex systems.
The article can be found here.
We are happy to announce that our work When is a network a network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks has been accepted for publication as a research paper at KDD'17. A short promotional video is available at the KDD YouTube channel:
Traditional research has investigated the controllability of complex networks - a property whether a system can be steered from an arbitrary initial state to any desired final state with admissible external inputs in a finite time. To implement control in practice, the trajectory (route) to reach the final state must be systematically understood. Here we uncover the relations between the trajectory and several key factors, such as the control time, distance between the initial and final states, and the number of nodes receiving external control inputs.
In our latest articles, we have provided a formal presentation of the generalised hypergeometric ensemble of random graphs (gHypEG), and we have introduced a new family of block models based on it that are naturally degree-corrected: the block-constrained configuration model (BCCM).
We have recently developed a new model to reproduce the size distribution of R&D alliances among firms. Our model can be used not only for agent-based simulations, but it is also analytic tractable. In addition, we have tested it against a data set listing 15,000 firms engaging in 15,000 R&D alliances over 26 years. Interested? Then take a look at our paper.
The "discovery" of reguralitiries and correlations from big data cannot replace the scientific clarification of the hidden causal effects. Hence to make real use of big data, social scientists are indespensable to make computational science a social one.
Read more about this on the ETH Zukunftblog.