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.
The next SG Seminar will take place on 4 May 2017
Prof. Dr. Thomas Gautschi (Universität Mannheim)
In our recent 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.
On 27th January 2017 the second symposium on Computational Social Science will take place.
More information on the symposium and the program are available here.
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.
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.
How do economic actors or scientists choose their collaboration partners? On one hand, one would argue that scientists as decision makers are quited different from firms. On the other hand, in order to reproduce macroscopic structure such as a collaboration network, we may not need to include all the microscopic details that distinguish economic from social agent.
In our preprint, we adopt a data-driven modeling approach to calibrate and validate a previously proposed agent-based model that abstract from these microscopic details, to capture only the essential features of the decision making process. The model is characterized by five parameters which relate to strategies adopted by economic actors or scientists when choosing their collaboration partners. Our results shed new light on the long-lasting question about the role of endogenous and exogenous factors in the formation of collaboration networks.
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.
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.
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.
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.
Every day scholars and online users explore available knowledge using recommender systems based on ranking algorithms. This challenge us to design more sophistcated filtering and ranking procedures to avoid biases that can systematically hide relevant contents.
In our recent work, we tackle this issue by quantifying and supressing biases of indicators of scientific impact. We use a large citation dataset from Microsoft Academic Graph and a new statistical framework based on the Mahalanobis distance to show that the rankings by well known indicators, including relative citation count and Google's PageRank score, are significantly biased by paper field and age. We propose a general normalization procedure motivated by the z-score which produces much less biased rankings when applied to citation count and PageRank score.
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
The developer portal JAXEnter has published an article covering one of our recent work From Aristotle to Ringelmann: a large-scale analysis of productivity and coordination in Open Source Software projects.
Our research article From Aristotle to Ringelmann: a large-scale analysis of productivity and coordination in Open Source Software projects has been covered by the IEEE Software blog of the IEEE Computer Society.
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.