
Course unit
NETWORK SCIENCE
INP9087851, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2019/20
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Core courses 
INGINF/03 
Telecommunications 
6.0 
Course unit organization
Period 
First semester 
Year 
1st Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Lecture 
6.0 
48 
102.0 
No turn 
Common characteristics of the Integrated Course unit
Prerequisites:

This course has the following prerequisites: knowledge in Probability Theory, and Computer Programming in any language which is appropriate for network analysis (e.g., MatLab, Python, C, Java, Linux). Moreover: 1. for the INTERNET module: to be familiar with the most basic networking and communication concepts and terms (ISO/OSI model, packetbased networks, routing); 2. for the NETWORK SCIENCE module: knowledge in Calculus and Linear Algebra; any further knowledge of networking processes in economics, biology, telecommunications, semantics, etc. might be useful. 
Target skills and knowledge:

The course is expected to provide the following knowledge and skills:
INTERNET module:
1. To know and understand the architecture of the Internet
2. To know the characteristics of the different types of data sources and their mathematical modeling
3. To understand the main protocol design principles
4. To know and understand the operating principles of the main network protocols (MAC, DLL, IP, UDP, TCP, FTP, HTTP)
5. To understand the role and functioning of the fundamental elements of the Internet, such as NAT, DHCP, DNS, SMPT servers
6. To master the mathematical tools need to dimension a simple communication network and evaluate its performance
7. To be able to set up and run a simple local network
8. To become familiar with fundamental tools for network configuration and diagnostics (packet sniffer, Tcpdump, ping, iperf, ifconfig,...)
NETWORK SCIENCE module:
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., tightlyknit groups), even partially overlapping, using proper algorithms
5. To evaluate the level of robustness/cohesion of a network
6. To know the main scenarios of application, possibly in crossdisciplinary contexts, of the techniques studied
7. To be able to summarise the analysis of a network in a professional paper
8. To be able to implement computer algorithms for network analysis 
Examination methods:

The course has the following methods of examination:
INTERNET module:
The final exam will be the same for both ATTENDING and NONATTENDING students since it does not rely on inclass activities. The exam consists of two parts, namely: 1. a WRITTEN EXAM at the computer, 2. a LAB TEST. Students will be offered four attempts to pass the written and the lab tests. During inclass lectures, the students may be offered to participate to some (in class or at home) activities, such as peerreviewing of other students' reports, participating inclass discussion and taking part to problemsolving competitions. The active participation to such initiatives may bring a few extra points (up to 3) to the students.
NETWORK SCIENCE module:
The verification of the expected knowledge and skills is carried out with the 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: 1. for ATTENDING students in which the students are guided towards intermediate project objectives (HOMEWORKS) coherently with the development of the lessons, and complete the project at the end of the course; 2. for NONATTENDING 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 four institutional dates. A bonus of up to 3 points is available for attending students that take part to an INTERDISCIPLINARY PROJECT with social science students attending the twin course on SOCIAL NETWORK ANALYSIS.
The final grade is expressed as a combination of the judgments in the two modules (50%+50%). 
Assessment criteria:

The evaluation criteria with which the verification of knowledge and expected skills will be carried out will be:
INTERNET module:
1. Completeness of the acquired knowledge
2. Level of understanding of the design principles of network protocols
3. Ability to discuss the pros and cons of the different network protocols
4. Ability to dimension a network through the proposed techniques
5. Knowledge of the technical terminology
6. Competence and coherence in the interpretation of performance curves and traces generated by network analytics tools
7. Capability of applying the learned knowledge to network problems other than those addressed in the course
8. Level of familiarity with basic network configuration and management tools
NETWORK SCIENCE module:
1. Completeness of the acquired knowledge
2. Ability to analyse 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 characteristics of the Module
Course unit contents:

The module will cover the following topics:
1. Network models  Basic network properties: graphs, adjacency matrix, degree distribution, connectivity; ErdosRenyi model; Random graphs with general degree distribution; Power laws and scale free networks; Small world phenomena; Hubs; Network generation and expansion; BarabasiAlbert model; Preferential attachment; Evolving networks; Assortativity; Robustness.
2. Ranking  Hubs and authorities; PageRank: teleportation, topic specific ranking, proximity measures, trust rank; Speeding up by quadratic interpolation.
3. Community detection  Dendrograms; Girvan Newman method and betweenness; Modularity optimization; Spectral clustering; Other clustering algorithms; Coreperiphery model for overlapping communities; Clique percolation method; Cluster affiliation model and BigCLAM.
4. Miscellaneous aspects  Link prediction; Applications scenarios 
Planned learning activities and teaching methods:

The module includes:
 a block of 18 lectures which will give an overview of the main subjects and methodologies, a discussion on the main scenarios of application, and a deeper mathematical analysis on some of the subjects introduced;
 6 laboratory lectures to guide students to the use of computer programs for network analysis.
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. AlbertLászló 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, AlbertLászló, Network Science. Cambridge: Cambridge University Press, 2016.

Newman, Mark E. J., Networks: an introduction. Oxford: New York, Oxford University Press, 2010.

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)
 Latex
 Matlab
Sustainable Development Goals (SDGs)

