Foundational Research on MULTIlevel comPLEX networks and systems
FP7 ICT/IP Project ICT-317532
Project web site -
Start-End Dates 01/11/2012- 30/11/2016
Coordinator This e-mail address is being protected from spambots. You need JavaScript enabled to view it
  • Scuola Imt (Istituzioni, Mercati, Tecnologie) Alti Studi Di lucca - Italy (IMT)
  • Universidade De Aveiro - Portugal (UAVR)
  • Bar Ilan University - Israel (BIU)
  • Universitat Rovira I Virgili - Spain (URV)
  • London Centre For Mathematical Sciences Lbg - United Kingdom (LIMS)
  • Kozep-Europai Egyetem - Hungary (CEU)
  • Centre National De La Recherche Scientifique - France (CNRS)
  • Eidgenoessische Technische Hochschule Zurich - Switzerland (ETHZ)
  • Aalto-Korkeakoulusaatio - Finland (AALTO)
  • Fondazione Istituto Per L'Interscambio Scientifico (I.S.I.) - Italy (ISI)
  • Universitaet Paderborn - Germany (UPB)
  • Medizinische Universitaet Wien - Austria (MUW)
  • Computer Technology Institute & Press Diophantus - Greece  (CTI)
  • Universita Degli Studi Di Roma La Sapienza - Italy (UNIROMA1)
  • Universidad De Zaragoza - Spain (UZ)
  • Uniwersytet Warszawski - Poland (UW)
  • Universitaet Wien - Austria (UNIVIE)


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 powergrid 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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