Faculty of Technical Sciences

Subject: Network science (17.EK553)

Native organizations units: No data
General information:
 
Category Scientific-professional
Scientific or art field Telecommunications and Signal Processing
Interdisciplinary No
ECTS 6
Educational goal:

Ability to apply the learned tools to model, analyze and solve a given real-world problem from the domain of networks. Ability to implement the learned methods and algorithms in a chosen experimental setup: USRP testbed or MATLAB/Python network data analysis software.

Educational outcome:

After successful completion of the course, the students will be able to: 1) apply the learned tools to model, analyze and solve a given real-world problem from the domain of networks; 2) implement the learned methods and algorithms in a chosen experimental setup: USRP testbed or MATLAB / Python network data analysis software.

Course content:

: Introduction to graph theory and algebraic graph theory, Adjacency matrix and graph Laplacian; Degree distribution and scale – free property; Clustering coefficients and centrality measures; Models of network formation: Erdos-Renyi random graph, Watts-Strogatz networks, Barabasi-Albert; Network communities and algorithms for their detection, stochastic block-model; Consensus algorithms, Industrial take on network science; Students’ project presentations and discussions.

Teaching methods:

Lectures, recitations, case study examples, lab sessions, reading assignments, homeworks, project This course is designed to balance and complement a principled, theoretical approach to network science with a highly practical and goal-oriented approach. In particular, the course will present the underlying theory in a mathematically rigorous manner (with carefully chosen proof derivations), but every new concept will be introduced, motivated and illustrated through a real-world problem and example. Further, there will be bi-weekly slots in the duration of one hour dedicated to detailed real-world case studies with emphasis on the most recent topics covered in lectures (WWW, Financial networks, Internet, etc., see [4]). Finally, lab sessions will be designed as hands-on tutorials for the relevant lab equipment (USRP testbed, software), which will gradually shift towards students’ individual, project driven work as the course progresses.

Literature:
Authors Title Year Publisher Language
Guido Caldarelli, Alessandro Chessa Data Science and Networks: Real Cases Studies with Python 2016 Oxford University Press English
Dimitry Zinoviev Complex Network Analysis in Python 2018 Pragmatic Bookshelf; 1st edition English
Ernesto Estrada, Philip Knight A First Course in Network Theory 2015 Oxford University Press English
Albert-László Barabási Network Science 2016 online: http://networksciencebook.com English
Knowledge evaluation:
Course activity Pre-examination Obligations Number of points
Written part of the exam - tasks and theory No Yes 50.00
Project Yes Yes 50.00
Lecturers:

vanr. prof. dr Bajović Dragana

Associate Professor

Lectures

Asistent Ninković Vukan

Assistant - Master

Computational classes

Istraživač pripravnik Petrović Nemanja

Intern Researcher

Computational classes

Asistent Lazić Ivan

Assistant - Master

Practical classes

Faculty of Technical Sciences

© 2024. Faculty of Technical Sciences.

Contact:

Address: Trg Dositeja Obradovića 6, 21102 Novi Sad

Phone:  (+381) 21 450 810
(+381) 21 6350 413

Fax : (+381) 21 458 133
Emejl: ftndean@uns.ac.rs

© 2024. Faculty of Technical Sciences.