Scheda programma d'esame
SOCIAL NETWORK ANALYSIS
(Social network analysis)
DINO PEDRESCHI
Anno accademico2022/23
CdSDATA SCIENCE AND BUSINESS INFORMATICS
Codice668AA
CFU6
PeriodoSecondo semestre
LinguaInglese

ModuliSettore/iTipoOreDocente/i
SOCIAL NETWORK ANALYSISINF/01LEZIONI48
DINO PEDRESCHI unimap
GIULIO ROSSETTI unimap
Programma non disponibile nella lingua selezionata
Learning outcomes
Knowledge

By the end of the course:

  • Students will have acquired knowledge about complex network modeling and analysis; 
  • Students will have acquired knowledge about the practical tools and methodologies available nowadays to represent data coming from heterogeneus sources with graphs and to perform standard analytical tasks.
Prerequisites

Suggested (optional) prerequisites:

  • data mining
  • python programming
Syllabus

 

The course is organized in three parts.

 

1st part: The Architecture of Complex Networks

  • Networks and Graphs
  • Random graphs
  • It’s a Small world
  • Scale Free Networks
  • Centrality & Assortative Mixing
  • Tie Strength & Resilience
  • High-order Network Analysis

2nd part: The Dynamics of Complex Networks

  • Community Discovery
  • Dynamic of networks
  • Link Prediction
  • Dynamic Community Discovery
  • Diffusion: Decision based models
  • Diffusion: Epidemics
  • Diffusion: Opinion Dynamics

3rd part: Case Studies

  • External Guest Lecture (TBD)
  • Polarization, Echo Chambers and Online Debates
  • SNA @ KDD Laboratory

 

NB: Hands-on tutorials covering the course themes are provided in Python and through Gephi/Cytoscape.

Bibliography

Reference books:

Additional resources (i.e., research papers) will be specified by the instructors during each lecture.

Non-attending students info

For non-attending students the same course program and assesment methods applies.

Assessment methods

The final exam comprises an individual written test and one group project to discuss orally. 

  • The written test can be replaced by successfully addressing two midterms.
  • Written test(s) cover the course's theoretical grounds, proposing practical exercises and open questions.
  • The group project (up to 3 members) involves data collection, network construction and characterization, deployment of a limited set of standard network tasks, and formulating and addressing an open research question on the collected data. All results need to be summarized and commented on in a written report.

 

Ultimo aggiornamento 10/10/2022 09:26