MULTIPLEX Foundational Research on MULTIlevel comPLEX networks and systems)

Multiplex is a project gathering together the prominent research institution at the intersection within Network Science, Complex Systems. Algorithms, Statistcal Physics and so forth. The main aim of this project is oriented to use the mathematical framework of Complex Networks and Algorithmics to establish a theoretical basis for the understanding, prediction and possibly control of the Complex Systems. This would be obtained by reconstructing their Dynamics from the huge and heterogeneous datasets available at different levels. The methods used will be derived from the approach developed in Graph Theory, Algorithmics and Statistical Physics. Validated Models will also be proposed

Future advancements in ICT domain are closely linked to the understanding about how multi-level complex systems function. Indeed, multi-level dependencies may amplify cascade failures or make more sudden the collapse of the entire system. Recent large-scale blackouts resulting from cascades in the power-grid coupled to the control communication system witness this point very clearly. A better understanding of multi-level systems is essential for future ICT’s and for improving life quality and security in an increasingly interconnected and interdependent world. In this respect, complex networks science is particularly suitable for the many challenges that we face today, from critical infrastructures and communication systems, to techno-social and socio-economic networks. 

MULTIPLEX proposes a substantial paradigm shift for the development of a mathematical, computational and algorithmic framework for multi-level complex networks. Firstly, this will lead to a significant progress in the understanding and the prediction of complex multi-level systems. Secondly, it will enable a better control, and optimization of their dynamics. By combining mathematical analyses, modelling approaches and the use of massive heterogeneous data sets, we shall address several prominent aspects of multi-level complex networks, i.e. their topology, dynamical organization and evolution. 

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