Complex Networks

Director: Guido Caldarelli

Curriculum overview

The theory of Complex Networks introduces a novel way to look at several natural and technological phenomena. In this curriculum we use this framework derived from the mathematics of Graph Theory, the database analysis of Computer Science and the modelling skills of Statistical Physics to describe some specific Natural Phenomena. The activities done are on

  • Analysis of Communities in Brain Networks
  • Reconstruction of Network of correlation from Time Series Analysis
  • Use of Bipartite Networks spectral properties for the clustering of patients with similar diseases
  • Definition of Chemical Networks
  • Study of the Root-Apices interaction in plants
  • Study of Channel Networks in plants

In all these activities there are a number of PhD projects for PhD students.

Input and Output Profiles

This curriculum aims at preparing researchers and professionals with a wide knowledge of the theoretical foundations and tools of database analysis and modelling of complex systems. Perspective students should preferably have a master-level background in computer science, engineering, physics, mathematics, statistics, economics, management science or in a related field. Graduates from the curriculum are qualified to work in universities, public and industrial research centers, and to take on professional roles and high-profile tasks and responsibilities in both private companies and public institutions.

Reference area(s): Computer Science (main), Economics and Management.

Research Units contributing to the curriculum: Networks - Complex Networks (main), AXES - Laboratory for the Analysis of Complex Economic Systems, LIME - Laboratory of Innovation Management and Economics, SysMA - System Modelling and Analysis.

PhD candidates also have the opportunity to collaborate with other institutions that work with IMT Research Units.

Coursework: See full course list