Subject

Introduction to Network Science

Course Title

Introduction to Network Science
Introduction to network science

Code

F18L3S087

Study Programme

—

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

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

Probability and Statistics or Fundamentals of Information Theory

10.

Objectives of the course programme (competences)

Introduction to the fundamental concepts of network science using real data. Equipping students to analyze the properties and dynamic processes in real complex networks and to model and visualize them. Study of fundamental methods for community detection, robustness assessment, optimization, data mining, and prediction in complex networks.

11.

Course content

Introduction to network science. Properties of complex and real networks: small-world effect, node transitivity, preferential attachment. Models of real networks. Social, information, biological, and techno-technological networks. Community detection and graphlets in complex networks. Robustness of complex networks through link and node analysis. Use of centrality measures and ranking algorithms. Paradoxes in social networks: status homophily, value homophily, social influence, external influences. Dynamic processes in complex networks: diffusion of influence, information, and contagion; consensus and synchronization. Game theory in social networks: monetization in social networks, social network formation, auctions, and target set selection. Multi-layered and time-varying complex networks: models, algorithms, and dynamic processes. Optimization of flow, transport, resource allocation, packaging, and routing in real networks. Data mining and prediction in large networks. Prediction of links, topology and attribute evolution. Prediction of outcomes of dynamic processes and traversal 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 available time

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

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

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

17.

Assessment method

17.1.	Tests	10 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	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.

Requirement for obtaining a signature and taking the final exam

Activities 15.1 and 15.2 completed

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

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