Knowledge discovery in big graph data

1.

Subject title

Knowledge discovery in big graph data

Откривање знаење во големи граф податоци

2.

Code

m23_s_025

3.

Study program

Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Еducation with ICT, Computer Science, Statistics and Data Analytics, Cloud Computing, IT management, Bioinformatics, Statistics and Data Analytics, Software Engineering, Internet Technologies and cyber security, Software Engineering, Security, Cryptography and Coding, Data science in computer science and engineering, Eco-informatics, Inteligent Systems, Software for embedded systems,

4.

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

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

доц. д-р Илинка Иваноска проф. д-р Кире Триводалиев проф. д-р Соња Гиевска

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

This course aims to introduce students in the field of knowledge detection in large graph organized data. Students will get acquainted with the challenges of processing large amounts of data, state -of -the -art methods and algorithms for graphs analysis, and the application of knowledge detection in large graph data in various application domains. Upon completion of the subject, students are expected to acquire: - a thorough understanding of the basics of knowledge detection in graph data - Ability to critical thinking about different methods and algorithms to extract knowledge - Ability to formulate and solve problems that can be replicated in the domain of graphs - Ability to analyze large data sets

11.

Subject content:

Introduction to Detecting Knowledge in Large Graf Data; Static graphs: laws and templates; Dynamic graphs: laws and templates; Link analysis: Random Walks, Pagerank, Hits; Classification of knots; The similarity of knots; Similarity of graphs; Alignment of graphs; Clustering graphs; Detecting non -collecting and overlapping communities; Prediction of relationships; Detection of anomalies; Frequent sub-grafts; Approximation and compression of graphs; Graphs representation; Deep learning in graphs; Areas of application: Detection of knowledge in protein interaction networks, detection of knowledge in brain networks, knowledge detection in social networks, knowledge detection in heterogeneous multimodal data, graph-based recommendation systems.

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

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

15 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

15 points

17.4.

Final exam

0 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

NULL

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

Механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature