NETWORK ANALYSIS

A surprisingly large quantity of situations can be conceptualized in terms if entities and of relations among them. In all these cases, it is useful to engage with network analysis. The contemporary world, with all its digital network and tools (able to record fine grained data) and social media generates an impressive quantity è information that can be conceptualized as networks. These data, if carefully analyzed can reveal surprising and valuable information about the subjects of analysis and about their relationships at different scales. Besides, understanding the topological characteristics of a social networks allows to understand and make predictions about its ability and efficiency in carrying out given actions or roles.

This module born in the context of Master in Data Analytics, has the objective to give the student the conceptual and practical tools to analyze social networks.

Upon successful conclusion of the module, the student:
• Will know the fundamental concepts related to social networks and will know how to contextualize them.
• Will know and be able to use the aforementioned concepts for the analysis of real network examples.
• Will know and be able to recognize the different network topologies, their characteristics and relationships.
• Will be able to independently analyze network data, and will be able to understand the results obtained.
• Will be able to run simple regression analyses including network data.

Mathematics (analysis, basics of linear algebra), Basic econometrics, Fundamentals of programming

The module has a structure associating in parallel the study of network theory with the empirical analysis of real networks through statistical software.
For what concerns the theoretical analysis, the module will start by introducing the concept of network and discussing the various forms of representation for network data. The basic measures necessary to understand the characteristics of a network will be then introduced, starting with monadic measures (degree) through dyadic measures (shortest paths, walk, diameter) and those based on larger subsets of network (clustering, connected components, assortativity, centrality).
The module will then have the objective to analyze the large-scale structure of networks, building and analyzing a taxonomy of the main network topologies, of their characteristics and their relationships. The objective is to allow the student to categorize the real networks approximating them with their theoretical structure. In particular we will deal with: random networks, small world networks and scale-free networks. We will derive the basic characterisics of these networks and analyze their consequences. Particular importance will be assigned to scale-free networks due to their empirical relevance.
For what concerns the empirical analysis, after having analyzed the basic characteristics of networks with R, we will study: how to divide a network according to his characteristics, , how to use a network to analyze from the econometric viewpoint systems where observations are highly correlated and cannot be assumed as independent.

Various book chapters and scientific papers as indicated in the moodle page of the module.
A non-comprehensive list includes:
- A.L. Barabasi and R. Albert 1999 “Emergence of scaling in random networks”
- M. Granovetter 1973: “The strength of weak ties”
- P. Erdos & A.Renyi 1959-1960 “On random networks” I and II
- Padgett & K. Ansell (1993) “Robust action and the rise of the Medici", American Journal of Sociology
- D.J. Watts & S.H. Strogatz. Collective dynamics of ’small-world’ networks
Several lectures will rely heavily on the book “Network Science”, A.L. Barabasi. The book can be purchased, or read line at http://networksciencebook.com/

Written report on the analysis of a network, or written exam. To be decided upon discussion with the class.

Frontal lectures, individual study, laboratory with the use of statistical software (in class).

English

written and oral

This subject deals with topics related to the macro-area "Circular economy, innovation, work" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development