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Risorsa bibliografica obbligatoria
Risorsa bibliografica facoltativa
Scheda Riassuntiva
Anno Accademico 2018/2019
Tipo incarico Dottorato
Insegnamento 053540 - COMPLEX NETWORKS
Docente Ardagna Danilo
Cfu 5.00 Tipo insegnamento Monodisciplinare

Corso di Dottorato Da (compreso) A (escluso) Insegnamento
MI (1380) - INGEGNERIA DELL'INFORMAZIONE / INFORMATION TECHNOLOGYAZZZZ053540 - COMPLEX NETWORKS
055127 - COMPLEX NETWORKS

Programma dettagliato e risultati di apprendimento attesi

This course provides the students with relevant topics in network analysis, from social networks to information and technological networks such as the Internet. We will present and revisit some important concepts on graph theory as well as discuss network science as an important tool for understanding several processes that pervade our daily lives. The course goals are to introduce basic concepts in network theory, to discuss metrics and models and to use software analysis tools for analyzing a wide variety of real-world network data.

Content

The course is structured in three parts.

  • The first part of the course covers the main concepts of graph theory and social networks.  The main goal is to introduce the basic graph definitions such as centrality measures, path and connectivity as well as the most traditional graph models applied on network science: random graphs, small-world model and Barabasi-Albert model. Furthermore, we will discuss how relationships between individuals impact community formation (homophily), structural balance (positive and negative relationships) and triadic closures (the role of strong and weak ties) on social networks.
  • The second part explores network dynamics. We will focus on how information flows in our society, both from a population model view (following the crowd approach) as well as structural model view (cascading behavior and small-world approaches). We will discuss the role of social capital on these processes.  At the end, we will explore some epidemiological models, and how relationships and individual’s behavior can potentialize the spread of the diseases.
  • The third part covers information networks and World Wide Web. Students will learn more about PageRank and Hits algorithms. We will also introduce the first model of the structure of the web, known as Bow-Tie model.

Note Sulla Modalità di valutazione

The assessment will be based on a research project developed by teams including two or three students. The evaluation of projects will be based on the produced report, following a scientific paper format.  In alternative, students can focus on a specific topic introduced during the course and provide a report on recent advances proposed in the state of the art.

 

 


Intervallo di svolgimento dell'attività didattica
Data inizio
Data termine

Calendario testuale dell'attività didattica
 

Bibliografia
Risorsa bibliografica facoltativaD. Easley, J. Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Editore: Cambridge University Press, Anno edizione: 2010 https://www.cs.cornell.edu/home/kleinber/networks-book/networks-book.pdf
Risorsa bibliografica facoltativaALBERT-LÁSZLÓ BARABÁSI, Network Science http://barabasi.com/book/network-science

Mix Forme Didattiche
Tipo Forma Didattica Ore didattiche
lezione
25.0
esercitazione
0.0
laboratorio informatico
0.0
laboratorio sperimentale
0.0
progetto
0.0
laboratorio di progetto
0.0

Informazioni in lingua inglese a supporto dell'internazionalizzazione
Insegnamento erogato in lingua Inglese
Disponibilità di materiale didattico/slides in lingua inglese
Disponibilità di libri di testo/bibliografia in lingua inglese
Possibilità di sostenere l'esame in lingua inglese
Disponibilità di supporto didattico in lingua inglese

Note Docente
schedaincarico v. 1.6.1 / 1.6.1
Area Servizi ICT
08/12/2019