Social Network and Big Data Analytics
Course Materials - Prof. Andrea Fronzetti Colladon

! Students will find here the materials related to the course lab.
! Only intended for those who have already attended the course in the classroom.

We live in a world where everything is connected: people, information, places, events, genes in the DNA. A clever way to make sense of these connection patterns is to study them as networks. Social Network Analysis (SNA) refers to widely used methods to study networks, which can be applied, for example, in social and behavioral sciences, in management and innovation management, economics, marketing, biology, psychology or engineering. The focus is on relationships among social entities; examples include communication or opinion formation among members of a group, economic transactions between corporations, or diffusion processes. Online, taking the Web as a mirror of the real world, SNA opens up unprecedented opportunities to read the collective mind, discovering emergent trends and behaviors. The possible applications are so many that they cannot be numbered.

Students will learn about the structure and evolution of networks, but other approaches will be not ruled out. The teaching will combine knowledge from a broad range of disciplines. The course covers Business Intelligence methods and focuses on data science and business analytics. The knowledge acquired from the study of SNA will be complemented with elements of Text Mining and Machine Learning.

The course is divided into three parts: the first theoretical; the second devoted to the analysis of case studies; the third practical, aimed at the application of what has been learned. Students will learn how to use specific software – such as, SBS BI, Condor, Pajek, Gephi, and the Python programming language.

Lab Materials

Python

• Innovative Measures

  • Demo for the calculation of the Semantic Brand Score (a different repository)
  • Demo for the calculation of Distinctiveness Centrality (a different repository)

• SNA with Python Networkx

  • Graph creation and drawing
  • Centrality metrics
  • Community detection

• Content Fetching

  • Python script for querying eventregistry.org

• Machine Learning

  • Catboost Classification
  • Catboost Regression
  • SHAP Explanations

Condor

• Condor lab instructions

Pajek and Gephi

• Pajek and Gephi lab instructions (italian)
• Pajek and Gephi lab instructions (english)

Contact

Andrea Fronzetti Colladon andreafc.com