First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Human and Social Sciences and Cultural Heritage
Course unit
SUP7079557, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
IF0315, Degree course structure A.Y. 2015/16, A.Y. 2019/20
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination SOCIAL NETWORK ANALYSIS
Department of reference Department of Linguistic and Literary Studies
E-Learning website
Mandatory attendance No
Language of instruction English
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Teacher in charge LEONARDO BADIA ING-INF/03
Other lecturers CATERINA SUITNER M-PSI/05

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Educational activities in elective or integrative disciplines SPS/08 Sociology of Culture and Communication 6.0

Course unit organization
Period First semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
Hours of
Individual study
Lecture 6.0 42 108.0 No turn

Start of activities 30/09/2019
End of activities 18/01/2020
Show course schedule 2019/20 Reg.2015 course timetable

Examination board
Board From To Members of the board
2 1920 01/10/2019 30/11/2020 BADIA LEONARDO (Presidente)
ERSEGHE TOMASO (Membro Effettivo)

Prerequisites: The course requires basic knowledge of mathematics (esp. probablity theory), in connection with proficiency with Sociology of Communications and/or Techniques for Social Inquiry.

Further knowledge of networking processes in economics, biology, telecommunications, semantics, etc. might be useful.
Target skills and knowledge: 1. To learn and critically interpret the main social network analytic measures
2. To understand different generative models and interactions in social networks
3. To be able to rank nodes in a network according to their level of importance
4. To use visualization methods for networks and communities
5. To know the main scenarios of application, possibly in cross-disciplinary contexts, of the techniques studied
6. To be able to apply network analysis in societal/cross-disciplinary contexts, with a holistic evaluation of its implications
Examination methods: The verification of knowledge and expected skills is carried out through an exam test divided into two parts:
1. DEVELOPMENT OF A PROJECT aimed at verifying the ability to apply theory in interdisciplinary contexts, and which requires: the choice, the collection of data, and the analysis of a social network (or a network linked to interdisciplinary subjects); the sociological evaluation of the results obtained; the drafting of an essay. The project is foreseen in two ways:
1a. for ATTENDING students in which the students are guided towards intermediate project objectives coherently with the development of the lessons, and complete the project at the end of the course;
1b. for NON-ATTENDING students, in which the development of the project takes place in a single solution.
2. ORAL EXAM that starts from the presentation of the project through slides, supplemented by the teacher's interventions aimed at testing the analytical ability of the student to recognize models and evaluate relevant metrics.
Assessment criteria: The evaluation criteria with which the verification of knowledge and expected skills will be carried out, and appropriately declined according to the articulation of the course, will be:
1. Completeness of the acquired knowledge
2. Ability to analyze a social network through the proposed techniques
3. Property in the technical terminology used, both written and oral
4. Originality and independence in the identification of the network under study
5. Competence and coherence in the interpretation of the meaning of the obtained results
6. Quality of oral exposition
Course unit contents: 1. Network models - Basic network properties: graphs, adjacency matrix, degree distribution, connectivity; Random graphs; Power laws and scale free networks; Small world phenomena; Hubs; Preferential attachment; Assortativity; Robustness.
2. Network epidemics and Compartmental Infection models; Network protection.
3. Ranking and communities - Hubs and authorities; PageRank: teleportation, topic specific ranking, proximity measures.
4. Social interaction. Homophily. Group dynamics.
5. Applications scenarios
Planned learning activities and teaching methods: The course will involve mainly: frontal lectures for the introduction of theoretical topics; some computer laboratory lectures to illustrate software and data visualization instruments; and group workshops for project discussion.
Additional notes about suggested reading: All the teaching material presented during the lectures is made available on the platform "".

Further educational material of interest can be found on the websites:
1. Albert-László Barabási, Network science,
2. Jure Lescovec, Analysis of Networks,
3. Remco van der Hofstad, Random graphs and complex networks,
Textbooks (and optional supplementary readings)
  • Barabási, Albert-László, Network science. Cambridge: Cambridge University Press, 2016. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Interactive lecturing
  • Working in group
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • One Note (digital ink)

Sustainable Development Goals (SDGs)
Quality Education Gender Equality Industry, Innovation and Infrastructure Reduced Inequalities Sustainable Cities and Communities