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it - Information Technology

Methods and Applications of Informatics and Information Technology

Editor-in-Chief: Conrad, Stefan / Molitor, Paul

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Volume 57, Issue 4


Understanding complex systems: When Big Data meets network science

Ingo Scholtes
Published Online: 2015-07-31 | DOI: https://doi.org/10.1515/itit-2015-0012


Better understanding and controlling complex systems has become a grand challenge not only for computer science, but also for the natural and social sciences. Many of these systems have in common that they can be studied from a network perspective. Consequently methods from network science have proven instrumental in their analysis. In this article, I introduce the macroscopic perspective that is at the heart of network science. Summarizing my recent research activities, I discuss how a combination of this perspective with Big Data methods can improve our understanding of complex systems.

Keywords: Complex networks; data mining; socio-technical systems; temporal networks

ACM CCS: Networks→Network properties→Network structure; Networks→Network properties→Network dynamics; Software and its engineering→Software creation and management→Collaboration in software development; Computing methodologies→Machine learning→Machine learning approaches

About the article

Ingo Scholtes

Ingo Scholtes is a senior researcher at the Chair of Systems Design at ETH Zürich. Following studies in computer science and mathematics, he completed his doctorate studies in the Systems Software and Distributed Systems group at the University of Trier in 2011. He was involved in the Large Hadron Collider experiment at CERN, designing and implementing a Peer-to-Peer-based framework for large-scale data distribution which is since being used to monitor particle collision data from the ATLAS detector. Inspired by this experience, he turned his attention to the modeling and analysis of complex networked systems. His latest research addresses applications of network science in the analysis of data from socio-technical systems, but also from biology and sociology. In a theoretical line of research he further studies new methods in the analysis of time-stamped network data. At ETH Zürich he developed a course on network science which bridges the curricula of engineering and natural sciences. He previously held a scholarship from the Studienstiftung des Deutschen Volkes and was awarded a Junior-Fellowship from the Gesellschaft für Informatik in 2014.

ETH Zürich, Chair of Systems Design, CH-8092 Zürich, Switzerland

Accepted: 2015-05-25

Received: 2015-03-31

Published Online: 2015-07-31

Published in Print: 2015-08-28

Citation Information: it - Information Technology, Volume 57, Issue 4, Pages 252–256, ISSN (Online) 2196-7032, ISSN (Print) 1611-2776, DOI: https://doi.org/10.1515/itit-2015-0012.

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