Faculty of Technical Sciences

Subject: Network science (17.EK553)

General information:
 
Category Scientific-professional
Scientific or art field Telecommunications and Signal Processing
ECTS 6

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.

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.

: 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.

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.

Authors Title Year Publisher Language
Dimitry Zinoviev Complex Network Analysis in Python 2018 Pragmatic Bookshelf; 1st edition English
Guido Caldarelli, Alessandro Chessa Data Science and Networks: Real Cases Studies with Python 2016 Oxford University Press 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
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

Assoc. Prof. Bajović Dragana

Associate Professor

Lectures

Assistant - Master Lazić Ivan

Assistant - Master

Practical classes

Assistant - Master Ninković Vukan

Assistant - Master

Computational classes

Professional Studies Professor Petrović Nemanja

Intern Researcher

Computational 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.