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 understand how networks form and evolve over time.
• Will understand, from a network perspective, important phenomena such as contagion, diffusion, or learning.
• Will become confident with the methodological tools useful to analyze network models.
• Will develop the ability to explore and understand further material on the subject in an independent way.

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

The analysis of social networks has become one of the liveliest fields of research in economics, as well as in other social sciences. This course aims at introducing the students to the wide field of complex networks and its link with economic and financial theory. 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 finally discuss a series of models of how networks form and impact economic agents behavior, including contagion, diffusion, learning, and peer influences.

A textbook is not required for the course. Various book chapters and scientific papers will be indicated in the Moodle page of the module.
A non-comprehensive list of textbooks that cover most the material for the course are

-Matthew O. Jackson Social and Economic Networks, Princeton University Press (Here are Princeton University Pressand Amazon pages for the book). Additional background readings, including research articles and several surveys on some of the topics covered in the course can be found onincludes:

-Newman, M. E. J. (2010). Networks: an introduction (Second edition). Oxford university press https://tinyurl.com/yk4dmhbl

-A.L. Barabasi. “Network Science”, The book can be purchased, or read line at http://networksciencebook.com/

-David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World, Cambridge University Press. ISBN: 9780521195331 (available online at https://www.cs.cornell.edu/home/kleinber/networks-book/ )

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

Frontal lectures, individual study, numerical exercises, class exercises using database and software, interactive class activities.

English

written and oral

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development