NETWORK SCIENCE

Academic year
2025/2026 Syllabus of previous years
Official course title
NETWORK SCIENCE
Course code
PHD192 (AF:552552 AR:330706)
Modality
ECTS credits
6
Degree level
Corso di Dottorato (D.M.226/2021)
Academic Discipline
SECS-S/03
Period
2nd Term
Course year
2
Where
VENEZIA
Moodle
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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.
Written
Regarding the grading scale (criteria for assigning grades): A. Scores in the range of 18-22 will be awarded for: - Sufficient knowledge and applied comprehension of the course material; - Sufficient ability to solve the given problems; - Sufficient proficiency in using Python; - Limited ability to explain the mathematical processes underlying the solutions of the proposed problems. B. Scores in the range of 23-26 will be awarded for: - Fair knowledge and applied comprehension of the course material; - Fair ability to solve the given problems; - Fair proficiency in using Python; - Fair ability to explain the mathematical processes underlying the solutions of the proposed problems. C. Scores in the range of 27-30 will be awarded for: - Good or excellent knowledge and applied comprehension of the course material; - Good or excellent ability to solve the given problems; - Good or excellent proficiency in using Python; - Good or excellent ability to explain the mathematical processes underlying the solutions of the proposed problems. D. Honors will be awarded for: - Outstanding knowledge and applied comprehension of the course material; - Excellent ability to solve the given problems; - Exceptional proficiency in using Python; - Excellent ability to present and explain the solutions to the proposed problems.
Lectures and tutorials. The tutorials illustrate the methods introduced and their application using network data from different domains and the software R.
Definitive programme.