Introduction to network science

Course title

Code

F18L3S087

Study programme

—

Organizer of the study programme (unit, institute, department)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First cycle

Academic year / semester

6 / Summer

Number of ECTS credits

Teacher

—

Prerequisites for enrolling in the course

Веројатност и статистика или Основи на теорија на информации

10.

Course objectives (competences)

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.

Course 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

Lectures supported by slide presentations, interactive lectures, practical classes (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defence of a project assignment and seminar paper, and learning in an electronic environment (forums and consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of the available time

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

15.

Forms of teaching activities

15.1.	Lectures – theoretical teaching	30 hours
15.2.	Exercises (laboratory, auditory), seminars, team work	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Individual assignments	15 hours
16.3.	Home study	75 hours

17.

Grading

17.1.	Tests	10 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activity and participation	10 points
17.4.	Final exam	70 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.

Requirements for signature and taking the final exam

Activities 15.1 and 15.2 completed

20.

Language of instruction

Macedonian and English

21.

Method of monitoring teaching quality

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature No. Author Title Publisher Year
22.2.	Additional literature No. Author Title Publisher Year