Teaching/Educational Activities
2018
An Introduction to Network Science 

15 Apr19 Apr 2019
Course for the ICT Doctoral School, by Alex Arenas (invited)
English [
More Info]
Network science has emerged as a branch of study focussing interest on the connectivity interactions between elements of a system. The most central object of study in network science are the socalled complex networks.
Complex weblike structures describe a wide variety of systems of high technological and intellectual importance. For example, the cell is best described as a complex network of chemicals connected by chemical reactions; the Internet is a complex network of routers and computers linked by various physical or wireless links; fads and ideas spread on the social network, whose nodes are human beings and whose edges represent various social relationships; the World Wide Web is an enormous virtual network of Web pages connected by hyperlinks. These systems represent just a few of the many examples that have recently prompted the scientific community to investigate the relationship between the topology of complex networks and the dynamics that take place on them.
A complex network is just a graph with several nontrivial topological properties, not present in simple models of networks. Some of them are: scalefree degree distributions, high clustering coefficients (i.e. more triangles than expected in a random network), assortativity (correlations between connected nodesâ€™ degrees), and community structure. On the contrary, simple graphs such as random networks or grids show a homogeneous structure in which all nodes are almost indistinguishable, unlike what is observed in real networks.
In this course we will review the stateoftheart in network science and put focus on the applications, and open problems faced so far.
Program:
 Lecture 1: Introduction to Complex Networks and its structural descriptors
 Lecture 2: Network models
 Lecture 3: Community structure in networks
 Lecture 4: Epidemic spreading in networks
 Lecture 5: Synchronisation in networks
MSc in Statistical Physics 

14 September  20 December 2018
Course:
Structure and Dynamics of Complex Networks
English [
Web]
Metabolic interactions within the cell, the human brain, largescale social and financial systems: what do they have in common? They are complex systems with a nontrivial structure and emergent dynamical properties, from collective phenomena to phase transitions.
The course aims at providing a basic introduction to modeling and analysis of complex networks, from the perspective of the physics of complex systems. The student will acquire new competences in the emergent field of Network Science, such as i) the development of generative models for disordered systems; ii) the analysis of topological properties influencing their transport dynamics (from random walks to synchronization of oscillators, to diffusion of pathogens or information in populations with heterogeneous connectivity); iii) the analysis of critical properties of interconnected systems; iv) the analysis of systems of systems, better known as multilayer networks.
Mediterranean School of Complex Networks 

18 September 2018
International Summer School, by Manlio De Domenico
English [
Web]
This course is thought as a first introduction to the framework of multilayer networks, i.e. graphs whose nodes are connected by links of different nature. The course will start with a detailed introduction of the multilayer framework, both from the theoretical and the applicative points of view, using the tensorial approach. In particular, it will be shown how to transfer all key concepts of graph theory into the multilayer networks, approaching some also some very recent results in the field. A number of applications to several scientific and socioeconomic scenarios will conclude the theoretical course. Finally, a handson tutorial will allow the student to reach a working knowledge of all the material.
2125 May 2018
Course for the ICT Doctoral School, by Manlio De Domenico
English [
Web]
This course is thought as a first introduction to the framework of multilayer networks, i.e. graphs whose nodes are connected by links of different nature. The course will start with a detailed introduction of the multilayer framework, both from the theoretical and the applicative points of view, using the tensorial approach. In particular, it will be shown how to transfer all key concepts of graph theory into the multilayer networks, approaching some also some very recent results in the field. A number of applications to several scientific and socioeconomic scenarios will conclude the theoretical course. Finally, a handson tutorial will allow the student to reach a working knowledge of all the material.
Program:
 Lecture 1: From Classical to Multilayer Networks (classification; mathematical formulation)
 Lecture 2: Dynamics in Multilayer Networks (spreading; robustness to attacks)
 Lecture 3: Multilayer Mesoscale Organization
 Lecture 4: Multilayer Descriptors
 Lecture 5: Timevarying Networks
1620 Apr 2018
Course for the ICT Doctoral School, by Alex Arenas (invited)
English [
More Info]
Network science has emerged as a branch of study focussing interest on the connectivity interactions between elements of a system. The most central object of study in network science are the socalled complex networks.
Complex weblike structures describe a wide variety of systems of high technological and intellectual importance. For example, the cell is best described as a complex network of chemicals connected by chemical reactions; the Internet is a complex network of routers and computers linked by various physical or wireless links; fads and ideas spread on the social network, whose nodes are human beings and whose edges represent various social relationships; the World Wide Web is an enormous virtual network of Web pages connected by hyperlinks. These systems represent just a few of the many examples that have recently prompted the scientific community to investigate the relationship between the topology of complex networks and the dynamics that take place on them.
A complex network is just a graph with several nontrivial topological properties, not present in simple models of networks. Some of them are: scalefree degree distributions, high clustering coefficients (i.e. more triangles than expected in a random network), assortativity (correlations between connected nodesâ€™ degrees), and community structure. On the contrary, simple graphs such as random networks or grids show a homogeneous structure in which all nodes are almost indistinguishable, unlike what is observed in real networks.
In this course we will review the stateoftheart in network science and put focus on the applications, and open problems faced so far.
Program:
 Lecture 1: Introduction to Complex Networks and its structural descriptors
 Lecture 2: Network models
 Lecture 3: Community structure in networks
 Lecture 4: Epidemic spreading in networks
 Lecture 5: Synchronisation in networks