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Humboldt-Universität zu Berlin - Faculty of Mathematics and Natural Sciences - IWCSN 2019

International Workshop on Complex Systems and Networks 2019

 

Berlin, September 23rd - 26th 2019

 

The IWCSN 2019 is the 16th International Workshop in a series of events organized consecutively in Bologna (2004), Hong Kong (2005), Vancouver (2006), Guilin (2007), Canberra (2008), Bristol (2009), Beijing (2010), Melbourne (2011), Minneapolis (2012), Vancouver (2013), Shanghai (2014), Perth (2015), Atlanta (2016), Qatar (2017), and Nanjing (2018).

 

The International Workshops on Complex Systems and Networks are strongly interdisciplinary workshops intended to bring together mathematicians, physicists, biologists, social scientists, Earth scientists and engineers working on different aspects of network dynamics. The focus of IWCSN 2019 will continue to be devoted to the impact of evolving network structure on collective dynamics. This area is currently a hot research topic in all branches of science and technology, thanks in part to the substantial recent progress about evolving networks, multilayer networks, distributed systems, nonlinear data analysis, machine learning, climate networks, infrastructure, social and financial networks, and neuronal networks.

 

The objectives of these workshops are to provide opportunities for participants to learn about state-of-the-art research in various related yet disparate fields. We plan to have tutorial talks in various areas and in-depth technical talks describing the latest research. Furthermore, they allow researchers and students from diverse disciplines to interact, find common ground, share results and insights, find out new problems and foster future collaboration.

 

Some of the main scientific questions that we would like to address in this workshop are:

 

  • What are the universality properties of complex networks?

  • What is the best complex network to deploy for a certain application?

  • How does the topology of the network influence the functioning of the underlying system?

  • What can we learn from biological, Earth system and social networks that can be useful in engineering networked systems, and vice versa?

  • What network models can be analyzed mathematically yet capture the salient features of the underlying ensemble systems?

  • Can we build a taxonomy of complex network models that facilitates the identification of phenomena in multilayer networks?

  • Are complex networks techniques useful to forecast extreme events?

  • How can we efficiently apply machine learning methods for treating large complex networks?

  • How to reconstruct reliably complex networks from experimental data?

 
Funded by:

 

DFG