This project delves into the intricate world of Social Network Analysis (SNA) and Network Science, presenting a comprehensive exploration of network properties and behaviors. Through rigorous statistical and inferential analysis, the project unveils patterns and insights into the structural characteristics of complex networks. This is a project for SOCIAL NETWORK ANALYSIS (INF/01) [https://www.unimi.it/en/education/degree-programme-courses/2021/social-network-analysis] course with Professor Gaito Sabrina Tiziana [https://scholar.google.com/citations?user=vUmucpoAAAAJ&hl=en].

The project's findings are based on detailed statistical analysis and include measures such as network size, order, average and maximum degrees, density, transitivity, and average clustering coefficient. A significant discovery was the identification of scale-free properties within the analyzed networks, indicated by the inverse relationship between degree and node count.

• Network size: Analysis of networks with varying sizes, highlighting the complexity and scope of network connections.
• Scale-free network: Evidence supporting the scale-free nature of the examined networks, emphasizing the presence of hub nodes with high connectivity.
• Network properties: Detailed examination of properties such as density, transitivity, and clustering coefficients, providing insights into the network's cohesion and structure.
• Inferential Analysis: Beyond mere statistical description, the project extends into inferential analysis to understand the underlying processes shaping these networks.

The project includes a range of visualizations, from network diagrams to distributions of network measures, aiding in the comprehension of complex network characteristics and the validation of scale-free properties.

This project underscores the importance of Social Network Analysis and Network Science in understanding complex networks' dynamics. It provides valuable insights into network behavior, which can be applied in various domains such as sociology, biology, computer science, and more.

For more information, collaboration, or questions regarding this project, please reach out:

Mehdi Abbassi
Email: [email protected]