SOCIAL NETWORK ANALYSIS

The course provides an introduction to social network analysis and its role in the study of complex systems. Through a theoretical and practical approach, it explores methods and techniques for big data analysis and social network modeling, with applications in various fields.

The course is designed to equip students with a methodological and applied foundation in network analysis, preparing them for more advanced courses in data science, network science, and computational analysis. Additionally, it integrates complementary skills from disciplines such as statistics, machine learning, and programming, fostering a multidisciplinary education in complex data analysis.

Knowledge and Understanding:
- Understand the fundamental principles of managing, manipulating, visualizing, and interpreting large datasets.
- Acquire theoretical and practical knowledge of network science, with a focus on network modeling and analysis.
- Understand the main social network analysis techniques, their theoretical foundations, and their applications.

Ability to Apply Knowledge and Understanding:
- Apply methods and tools for managing and analyzing structured and unstructured data.
- Develop and perform social network analyses using appropriate software and programming languages.
- Critically interpret the results of network analyses and communicate them clearly and rigorously.

Learning Skills:
- Be able to consult and interpret technical documentation on network analysis tools.
- Develop a critical approach to data usage and network analysis methodologies, with the ability to independently explore new tools and techniques.

A basic knowledge of the following topics is required:
- Programming: fundamental concepts of structured programming and experience with at least one programming language (e.g., Python).
- Statistics: elements of descriptive statistics, basic probability, and essential notions of statistical inference.

- Introduction to Graph Theory
- Social Networks
- Strong and Weak Ties
- Homophily and Social Influence
- Network Dynamics
- The Small-World Phenomenon
- Echo chambers and polarization

(This outline may be subject to change.)

Easley, David, and Jon Kleinberg. Networks, crowds, and markets. Cambridge University Press, 2010.
Barabási, Albert-László. Network Science. Cambridge University Press, 2016

The final exam consists of an oral examination and the completion of a project agreed upon with the instructor.

1) Oral Examination (30%)
The oral exam (approximately 40 minutes) is divided into two parts:
- Theoretical questions and exercises on the topics covered in the course.
- Discussion of the project previously agreed upon with the instructor.
The assessment of the oral exam will be based on expressive skills, accuracy of language, and theoretical knowledge.

2) Project (70%)
Students are required to develop an original analysis by applying the methods, tools, and techniques covered during the course.
- The project can be carried out in groups of up to two students.
- The project evaluation will consider the accuracy of execution according to the guidelines and the technical complexity.

oral

Grades 18-21: Sufficient
- Oral exam: Partial understanding of theoretical topics, uncertain expression with language inaccuracies. Answers are fragmented or incomplete.
- Project: Minimal adherence to guidelines, basic analysis with possible methodological errors. Limited technical complexity but sufficient to demonstrate a fundamental application of the covered concepts.

Grades 22-24: Fair
- Oral exam: Correct but not always in-depth answers, adequate language with some inaccuracies. Good ability to apply the topics.
- Project: Analysis consistent with the guidelines, appropriate methodology, but lacking significant depth or originality. Moderate technical complexity.

Grades 25-27: Good
- Oral exam: Precise and well-argued answers, good use of technical language, confident presentation.
- Project: Well-structured and accurate analysis, solid application of course methods and tools. Well-developed technical complexity with elements of originality.

Grades 28-30: Very Good/Excellent
- Oral exam: Complete and well-structured answers, excellent mastery of topics, and appropriate use of technical language. Strong critical and interdisciplinary reasoning skills.
- Project: In-depth and original analysis, rigorous application of methods with potential extensions or improvements. High technical complexity and advanced problem-solving skills.

30 with Honors
- Excellence in both the oral exam and the project, demonstrating advanced critical and analytical skills, flawless presentation, and a particularly innovative contribution in the project.

The course is based on interactive lectures, where theoretical concepts are introduced and explored in depth through practical examples and guided discussions.

The lectures combine:
- Theoretical presentation of the fundamental principles of network analysis and its methodologies.
- Practical examples, used to concretely illustrate the application of the methods and tools covered.
- Student interaction, encouraging active participation through questions, critical reflections, and case study analysis.