MULTIPLEX: Foundational Research on Multilevel Complex Networks and Systems
This project contributes to our research line: Multi-layered networks
Duration: 48 months (November 2012 - November 2016)
Funding program: EU 7th Framework Programme. FET Proactive IP Project number 317532. 2012-2016.
Project partners: IMT Alti Studi Lucca (Italy), Universidad de Aveiro (Portugal), Bar-Ilan University (Israel), Universitat Rovira I Virgili (Spain), London Institute for Math. Sciences (UK), Central European University (Hungary), CNRS (France), ETH Zurich (Switzerland), Aalto University (Finland), ISI Torino (Italy), Paderborn University (Germany), Medical Institute of Wien (Austria), Computer Technology Institute & Press Diophantus (Greece), University Sapienza (Italy), University of Zaragoza (Spain), University of Warsaw (Poland), University of Wien (Austria), Aristotle University of Thessaloniki (Greece), University of Lausanne (Switzerland), Jozif Stefan Institute (Slovenia),Ruder Boskovic Institute (Croatia),University of Leiden (Netherlands).
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. 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.
Complex networks science is particularly suited to shed new light on the structural and dynamical interrelations between infrastructure and communication networks and between techno-social and socio-economic networks. MULTIPLEX proposes 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. Combining these modelling approaches with the analysis of massive heterogeneous data sets will lead to profound insights into the topology, dynamical organization and evolution of multi-level complex networks.
Garas, Antonios; Tomasello, Mario Vincenzo; Schweitzer, Frank