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agina del corso

Fondamenti di automatica

(Ing. Matematica, L, 10 cfu, 2 sem)

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ComplessitÓ nei sistemi e nelle reti

(LM, 5 cfu, 1 sem, 2 emisem)

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Dinamica dei sistemi complessi

(LM, corso integrato, 5+5 cfu, 1 sem)

[info per studenti delle lauree magistrali]

Se siete interessati a studi di carattere generale riguardanti la dinamica dei sistemi complessi e le loro applicazioni, in particolare a temi quali la dinamica non lineare, il caos, le reti complesse, i sistemi adattativi, potete considerare di inserire nel vostro piano di studi qualcuno di questi corsi, attivi nell'a.a. 2016/17 presso il Campus Leonardo:

Teoria dei sistemi (Dinamica non lineare) (5 cfu, 1 sem, 1 emisem), docente Sergio Rinaldi
Systems theory (Nonlinear dynamics) (5 cfu, 1 sem, 1 emisem), docente Fabio Dercole
ComplessitÓ nei sistemi e nelle reti
(5 cfu, 1 sem, 2 emisem), docente Carlo Piccardi

Dinamica dei sistemi complessi
(5+5 cfu, 1 sem),
corso integrato

Per qualunque informazione in merito a questi corsi sieti invitati a rivolgervi ai rispettivi docenti.

Last update: May 23, 2017

Carlo Piccardi

DEIB - Dipartimento di Elettronica, Informazione e Bioingegneria
Politecnico di Milano
Piazza Leonardo da Vinci 32
20133 Milano, Italy

tel (++39) 02 2399 3566
fax (++39) 02 2399 3412
e-mail carlo.piccardi@polimi.it
web page http://home.deib.polimi.it/piccardi/

Politecnico di Milano, 10-11-12, 17-18-19 Oct 2017
Lyon, France, 29 Nov-1 Dec 2017

Click here for details on recent research activities (some with codes and data available).

Check the list of my publications on peer reviewed journals (or visit my Google Scholar page).

    [research highlights]
F. Calderoni, D. Brunetto, and C. Piccardi,
Communities in criminal networks: A case study,
Social Networks, 48, 116-125, 2017. [doi]

Criminal organizations tend to be clustered to reduce risks of detection and information leaks. Yet, the literature exploring the relevance of subgroups for their internal structure is so far very limited. The paper applies methods of community analysis to explore the structure of a criminal network representing the individuals' co-participation in meetings. It draws from a case study on a large law enforcement operation (``Operazione Infinito'') tackling the 'Ndrangheta, a mafia organization from Calabria, a southern Italian region. The results show that the network is indeed clustered and that communities are associated, in a non trivial way, with the internal organization of the 'Ndrangheta into different ``locali'' (similar to mafia families). Furthermore, the results of community analysis can improve the prediction of the ``locale'' membership of the criminals (up to two thirds of any random sample of nodes) and the leadership roles (above 90% precision in classifying nodes as either bosses or non-bosses). The implications of these findings on the interpretation of the structure and functioning of the criminal network are discussed.

G. Berlusconi, F. Calderoni, N. Parolini, M. Verani, and C. Piccardi,
Link prediction in criminal networks: A tool for criminal intelligence analysis

, 11(4): e0154244, 2016.

The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activities.
I. Cingolani, C. Piccardi, and L. Tajoli, Discovering preferential patterns in sectoral trade networks,
PLoS ONE, 10(10)
, e0140951, 2015. 

We analyze the patterns of import/export bilateral relations, with the aim of assessing the relevance and shape of "preferentiality" in countries' trade decisions. Preferentiality here is defined as the tendency to concentrate trade on one or few partners. With this purpose, we adopt a systemic approach through the use of the tools of complex network analysis. In particular, we apply a pattern detection
approach based on community and pseudocommunity analysis, in order to highlight the groups of countries within which most of members' trade occur. The method is applied to two intra-industry trade networks consisting of 221 countries, relative to the low-tech "Textiles and Textile Articles" and the high-tech "Electronics" sectors for the year 2006, to look at the structure of world trade before the start of the international financial crisis. It turns out that the two networks display some similarities and some differences in preferential trade patterns: they both include few significant communities that define narrow sets of countries trading with each other as preferential destinations markets or supply sources, and they are characterized by the presence of similar hierarchical structures, led by the largest economies. But there are also distinctive features due to the characteristics of the industries examined, in which the organization of production and the destination markets are different. Overall, the extent of preferentiality and partner selection at the sector level confirm the relevance of international trade costs still today, inducing countries to seek the highest efficiency in their trade patterns.


I am Full Professor at Politecnico di Milano. My teaching activity is in the area of complex systems and networks, dynamical systems and automatic control, both to undergraduate and graduate students of the Politecnico schools of engineering. I also teach in the PhD program of my Department (DEIB), mostly in the area of complex systems.

My research interests are focused on several topics concerning complex systems. Some recent areas of research are:

  • Structural properties of networks, with applications in economics, finance, and social sciences;
  • Spreading processes on complex networks;
  • Synchronization of networks of dynamical systems;
  • Reduced models for nonlinear systems, and their use for solving control and parameter estimation problems; 
  • Application of optimal control techniques to nonlinear systems; 
  • The use of numerical methods for bifurcation analysis of nonlinear systems, including continuation methods and harmonic-balance techniques; 
  • The analysis of structural properties of cooperative systems.