Introduction to network science

Introduction to network science

1.

Subject title

Introduction to network science

Вовед во мрежна наука

2.

Code

F23L3S087

3.

Study program

Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Информатичка едукација, Software engineering and information systems, Примена на информациски технологии, Софтверско инженерство и информациски системи, Компјутерски науки, Компјутерско инженерство, Интернет, мрежи и безбедност, Software engineering and information systems, Стручни студии за програмирање, Стручни студии за програмирање,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Прв циклус

6.

Academic year / semester

3 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Игор Мишковски ворн. проф. д-р Мирослав Мирчев

9.

Prerequisites for enrollment

Веројатност и статистика или Основи на теорија на информации или Математика 3 или Бизнис статистика

10.

Subject goals and competencies:


Students will be introduced to concepts in Network Science on real data. At the end of the course the student would be able to analyze different properties and dynamical processes in real complex networks, and they would be able to model and visualize networks and dynamical processes on networks. Students throughout the course will learn the basic methods for community detection, robustness evaluation, network optimization, data mining and prediction in networks.

11.

Subject content:


Introduction to Network Science. Properties in complex and real-data networks: small-world phenomenon, node transitivity, preferential attachment. Real-data network models. Social, information, biological and technological networks. Community detection and graphlets in complex networks. Node and edge analysis of network robustness. Centrality measures and ranking algorithms. Social network paradoxes: status homophily, value homophily, social influence, external influence. Dynamical processes in complex networks: influence spreading, information and virus spreading, consensus and synchronization. Game theory in social networks: monetization in social networks, social network formation, bidding and target set selection. Multilayer and temporal complex networks: models, algorithms and dynamical processes. Flow optimization, resource distribution, packing and routing in real networks. Data mining and prediction in massive complex networks. Link and topology prediction. Prediction of the dynamical processes` outcome and traversing of complex networks..

12.

Learning methods:


Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

30 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

45 hours

16.

Other forms of activities

16.1.

Project tasks

15 hours

16.2.

Independent tasks

15 hours

16.3.

Homework

75 hours

17.

Grading method

17.1.

Tests

10 points

17.2.

Seminar work / project (presentation: written and oral)

15 points

17.3.

Activities and learning

10 points

17.4.

Final exam

50 points

18.

Grading criteria (points / grade)

up to 50 points

5 (five) (F)

from 51 to 60 points

6 (six) (E)

from 61 to 70 points

7 (seven) (D)

from 71 to 80 points

8 (eight) (C)

from 81 to 90 points

9 (nine) (B)

from 91 to 100 points

10 (ten) (A)

19.

Condition for signature and taking final exam

Реализирани активности 15.1 и 15.2

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

No.

Author

Title

Publisher

Year

2876

Barabási, Albert-László

Network science

Cambridge university press

2016

2877

Lewis, Ted G.

Network science: Theory and applications

John Wiley & Sons

2011

2878

Newman, Mark

Networks: an introduction

Oxford university press

2010

2879

David Easley and Jon Kleinberg

Networks, Crowds, and Markets: Reasoning About a Highly Connected World

Cambridge University Press

2010

2880

Guido Caldarelli, Alessandro Chessa

Data Science and Complex Networks: Real Cases Studies with Python

Oxford University Press

2014

2881

William L. Hamilton

Graph Representation Learning

Мorgan&Claypool Publishers

2020

2882

Claudio Stamile , Aldo Marzullo , Enrico Deusebio

Graph Machine Learning

Packt Publishing

2021

22.2.

Additional literature

No.

Author

Title

Publisher

Year