Complex systems and networks: theory and applications


This research area pursues methodological and applicative advancements in the broad field of complex networks and systems. The common feature to these systems is the inability to explain the collective behavior of the whole system on the basis of the study of the behavior of its single constituent elements. Methodological efforts are mostly focused on the theory of coordination and collective phenomena, of which consensus theory and flocking are paradigmatic examples. The items below provide some application examples that are encompassed by this activity.

  • socio-technical systems and epidemic processes, in particular collective motion, opinion dynamics, spreading of innovation, social interactions, human behavior, and epidemic spreading. Especially concerning the latter topic, a mixture of techniques –analytical, simulative, empirical, data-driven – are leveraged to obtain reliable and realistic predictive models for epidemic spreading in spatially-extended populations, in the attempt of conjugating some analytical tractability with realistic features and prediction accuracy.
  • characterization and modeling of technical networks such as mixed electric/gas networks for energy and power distribution. Graph-theoretic and surrogate model development for fast prediction and statistical performance assessment.
  • definition and analysis of efficient cooperative, distributed algorithms over networks. Examples are ranking algorithms that receive noisy pairwise evaluations as input, distributed averaging algorithms, consensus algorithms, and distributed estimation.
  • development of novel computation paradigms, mostly related to the onset of quantum computing, an approach that despite the huge efforts still has to deliver results closer to possible applications. Important advantages in terms both of computational scalability, speed and energy consumption can however be devised also exploiting non-quantum approaches, in which the information processing procedure is encoded in the evolution of a nonlinear (complex) dynamical system embedding properly chosen features. For instance, networks of coupled autonomous oscillators for the implementation of computational functions.

ERC sectors 

  • PE7_1 Control engineering
  • PE7_2 Electrical engineering: power components and/or systems
  • PE7_3 Simulation engineering and modelling
  • PE7_8 Networks, e.g. communication networks and nodes, Internet of Things, sensor networks, networks of robots
  • PE7_9 Man-machine interfaces
  • PE1_20 Control theory, optimisation and operational research
  • PE6_7 Artificial intelligence, intelligent systems, natural language processing
  • PE6_4 Theoretical computer science, formal methods, automata


  • Complex systems
  • Complex networks
  • Consensus theory
  • Digital epidemiology
  • Collective phenomena
  • Social networks
  • Opinion dynamics
  • Cooperative algorithms over networks  
  • Unconventional computing
  • Analog computing
  • Nonlinear dynamical systems