First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
ICT FOR INTERNET AND MULTIMEDIA
Course unit
NETWORK SCIENCE
INP7080669, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
ICT FOR INTERNET AND MULTIMEDIA
IN2371, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
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Degree course track CYBERSYSTEMS [002PD]
Number of ECTS credits allocated 9.0
Type of assessment Mark
Course unit English denomination NETWORK SCIENCE
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN2371-002PD-2018-INP7080669-N0
Mandatory attendance No
Language of instruction English
Branch PADOVA
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

Lecturers
Teacher in charge TOMASO ERSEGHE ING-INF/03
Other lecturers LEONARDO BADIA ING-INF/03

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP7080669 NETWORK SCIENCE TOMASO ERSEGHE IN2371
INP7080669 NETWORK SCIENCE TOMASO ERSEGHE IN2371
INP7080669 NETWORK SCIENCE TOMASO ERSEGHE IN2371
SUP7079557 SOCIAL NETWORK ANALYSIS -- IF0315

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/03 Telecommunications 9.0

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

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 9.0 72 153.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 18/01/2019

Examination board
Board From To Members of the board
2 A.A. 2018/2019 01/10/2018 15/03/2020 ERSEGHE TOMASO (Presidente)
BADIA LEONARDO (Membro Effettivo)
CALVAGNO GIANCARLO (Supplente)
CORVAJA ROBERTO (Supplente)
GUGLIELMI ANNA VALERIA (Supplente)
LAURENTI NICOLA (Supplente)
MILANI SIMONE (Supplente)
ROSSI MICHELE (Supplente)
TOMASIN STEFANO (Supplente)
ZANELLA ANDREA (Supplente)
ZANUTTIGH PIETRO (Supplente)
ZORZI MICHELE (Supplente)
1 A.A. 2017/2018 01/10/2017 15/03/2019 BADIA LEONARDO (Presidente)
ERSEGHE TOMASO (Membro Effettivo)
BENVENUTO NEVIO (Supplente)
CALVAGNO GIANCARLO (Supplente)
CORVAJA ROBERTO (Supplente)
LAURENTI NICOLA (Supplente)
MILANI SIMONE (Supplente)
ROSSI MICHELE (Supplente)
TOMASIN STEFANO (Supplente)
VANGELISTA LORENZO (Supplente)
ZANELLA ANDREA (Supplente)
ZANUTTIGH PIETRO (Supplente)
ZORZI MICHELE (Supplente)

Syllabus
Prerequisites: WARNING! The course has two different articulations: NETWORK SCIENCE (9 credits) is given in the MSc degree in "ICT for Internet and multimedia", while SOCIAL NETWORK ANALYSIS (6 credits) is given in the master's degree in "Communication strategies," sharing only part of the contents with the NETWORK SCIENCE course, and completing them with appropriate laboratory and project activities. The purpose of the two articulations is clearly different, and therefore the distinction is maintained in all the entries of the present syllabus.
--------------------------------

In its various articulations the course has the following prerequisites:
NETWORK SCIENCE - The course requires knowledge in Calculus and Linear Algebra, Probability Theory, and Computer Programming in any language which is appropriate for network analysis (e.g., MatLab, Python, C, Java).
SOCIAL NETWORK ANALYSIS - The course requires basic knowledge of Mathematics/Statistics, 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: In its various articulations the course is expected to provide the following knowledge and skills:
NETWORK SCIENCE
1. To learn and critically interpret the main network analytic measures
2. To be aware of the main mathematical models describing network generation processes
3. To be able to rank nodes in a network according to their level of importance
4. To identify communities (i.e., tightly-knit groups), even partially overlapping, using proper algorithmics
5. To know and be able to use the main diffusion models in a network
6. To evaluate the level of robustness/cohesion of a network
7. To know the main scenarios of application, possibly in cross-disciplinary contexts, of the techniques studied
8. To be able to summarize the analysis of a network in a professional paper
9. To be able to implement computer algorithms for network analysis
SOCIAL NETWORK ANALYSIS
1. To learn and critically interpret the main network analytic measures
2. To be able to rank nodes in a network according to their level of importance
3. To identify communities (i.e., tightly-knit groups), even partially overlapping
4. To evaluate the level of robustness/cohesion of a network
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: In its various articulations, the course has the following methods of examination:
NETWORK SCIENCE
The verification of the expected knowledge and skills is carried out with 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 different network for each student; computer implementation (in any programming language known to the student) of the algorithms required for the analysis; 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 and is discussed in an oral exam in one of the institutional dates.
2. Written exam (in open book mode) consisting of 3 exercises that require to calculate analytically and / or numerically some network measures, aimed at verifying the analytical ability of the student to recognize models and evaluate relevant metrics; specifically, the exercises will focus on: analysis of network; epidemiological models; analysis of the importance of the nodes of a network; community identification; robustness of the network.
SOCIAL NETWORK ANALYSIS
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.

The final grade is expressed as a combination of the judgments in the two parts (30% project, 70% exam) and provides a bonus of up to 3 points for attending students (point 1a). The exam sessions will be organized as follows: the written exam will take place in the morning, while the afternoon will be dedicated to the oral discussion for the students of SOCIAL NETWORK ANALYSIS and the NON-ATTENDING students of NETWORK SCIENCE.
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 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 analytical measures
6. Ability in the use of IT tools in the study of network analytical measures (specific for NETWORK SCIENCE)
7. Quality of oral exposure (specific for SOCIAL NETWORK ANALYSIS)
Course unit contents: In its various articulations the course will cover the following topics:
NETWORK SCIENCE
1. Network models - Basic network properties: graphs, adjacency matrix, degree distribution, connectivity; Erdos-Renyi model; Random graphs with general degree distribution; Power laws and scale free networks; Small world phenomena; Hubs; Network generation and expansion; Barabasi-Albert model; Preferential attachment; Evolving networks; Assortativity; Robustness.
2. Network epidemics and Compartmental model - SIR model; Infection models; Extensions of SIR; Heterogeneity; Complex contagion; Network protection.
3. Ranking - Hubs and authorities; PageRank: teleportation, topic specific ranking, proximity measures, trust rank; Speeding up by quadratic interpolation.
4. Community detection - Dendrograms; Girvan Newman method and betweenness; Modularity optimization; Spectral clustering; Other clustering algorithms; Core-periphery model for overlapping communities; Clique percolation method; Cluster affiliation model and BigCLAM.
5. Miscellaneous aspects - Link prediction; Cascading behavior; Influence maximization; Outbreak detection.
6. Applications scenarios
SOCIAL NETWORK ANALYSIS
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 model - SIR model; Infection models; Network protection.
3. Ranking - Hubs and authorities; PageRank: teleportation, topic specific ranking, proximity measures.
4. Community detection - Modularity optimization; Spectral clustering; Core-periphery model for overlapping communities; Cluster affiliation model.
5. Applications scenarios
Planned learning activities and teaching methods: To manage its various articulations, the course includes a common block of 12 lectures (3 credits) which will give an overview of the main subjects and methodologies, and which will also include a discussion on the main scenarios of application. Depending on the course articulation, the remaining lectures are organized as follows:
NETWORK SCIENCE
18 lectures will cover a deeper mathematical analysis on the subjects introduced in the common part, and will also include the presentation of more advanced topics; 3 lectures will be dedicated to the solution of exercises; 3 lectures will be dedicated to the ongoing review of projects.
SOCIAL NETWORK ANALYSIS
8 laboratory lectures will be given to guide students to the use of computer programs for network analysis, and 4 lectures will be dedicated to ongoing review of projects.

The frontal teaching activities involve the use of tablet computers (transparencies + digital ink).
Additional notes about suggested reading: All the teaching material presented during the lectures is made available on the platform "http://elearning.dei.unipd.it".

Further educational material of interest can be found on the websites:
1. Albert-László Barabási, Network science, http://barabasi.com/networksciencebook
2. Jure Lescovec, Analysis of Networks, http://web.stanford.edu/class/cs224w
3. Remco van der Hofstad, Random graphs and complex networks, http://www.win.tue.nl/~rhofstad/NotesRGCN.html
Textbooks (and optional supplementary readings)
  • Barabási, Albert-László, Network Science. Cambridge: Cambridge University Press, 2016. Cerca nel catalogo
  • Newman, Mark E. J., Networks: an introduction. Oxford: New York, Oxford University Press, 2010. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • 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