NETWORK SCIENCE

Network Science is a distinct domain of data science concerned with collecting, representing, and analyzing relational data. This type of data is gathered by recording relationships between pairs of entities. It is usually represented as a graph, where the nodes are entities, and the edges are the relationships among them. The bulk of the discipline is analyzing and explaining the dependencies among the pairs of entities and how these dependencies might affect individual outcomes.
This course reviews basic network concepts and focuses on the statistical models to analyze cross-sectional and longitudinal relational data. We illustrate the applicability of these models by using examples from several disciplines (e.g., Economics, Organizational, and Social Sciences) and the software R.

By the end of this course, students will be able to apply descriptive statistics and stochastic models to analyze relational data in various contexts.
In particular, students can:
- describe the introduced methods and analyze the commonalities and differences among them
- identify adequate methods to analyze relational data to answer a specific research question
- perform statistical analysis using the software R: descriptive analysis, parameter estimation, interpretation, and critical assessment of the results obtained
- explain the models and communicate the results to an audience that might be unfamiliar with

- Course “Mathematics for management studies”, prof. Marco Tolotti
- A sound understanding of estimation methods, hypothesis testing and linear regression models (OLS)

The course covers the following topics:
- Introduction to relational data, notation and basic concepts, software R
- Network descriptive statistics (Degree distributions, Centrality, Clustering)
- Introduction to network modeling
- Exponential Random Graph Models
- Stochastic actor-oriented models for the co-evolution of networks and individual outcomes
- Extensions of the introduced models and other models

- Slides and additional readings provided by the instructor
- Robins, G., Pattison, P., Kalish, Y., and Lusher, D. (2007). An introduction to exponential random graph ($p^*$) models for social networks. Social networks, 29(2): 173-191.
- Lusher, D., Koskinen, J., and Robins, G. (Eds.). (2013). Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press.
- Snijders, T. A. B., Van de Bunt, G.G., and Steglich, C. (2010). Introduction to stochastic actor-based models for network dynamics. Social networks 32(1): 44-60.

A short paper (maximum 3,000 words) consisting of the analysis of network data in a specific domain agreed upon with the lecturer.

Lectures and tutorials. The tutorials illustrate the methods introduced and their application using network data from different domains and the software R.

English

Written