Managing and Mining Complex Networks
| Title | Διαχείριση και Εξόρυξη Πολύπλοκων Δικτύων / Managing and Mining Complex Networks |
| Code | DWS108 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Apostolos Papadopoulos |
| Common | No |
| Status | Active |
| Course ID | 600020700 |
Programme of Study: PMS EPISTĪMĪ DEDOMENŌN KAI PAGKOSMIOU ISTOU (2018 éōs sīmera) PF
Registered students: 12
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 1 | 1 | 7.5 |
| Academic Year | 2023 – 2024 |
| Class Period | Winter |
| Faculty Instructors | |
| Weekly Hours | 3 |
| Class ID | 600239497
|
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600239497
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=15774
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Learning Outcomes
1. Students will get important knowledge in managing and mining complex networks.
2. They will work in teams
3. They will be more confident by presenting their work in class
4. They will get in contact with theory and algorithms for managing and knowledge discovery from complex networks.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work autonomously
- Work in teams
- Generate new research ideas
Course Content (Syllabus)
graph management, analysis and visualization systems: Neo4j, Graphlab, GraphX, Gephi
reachability queries
graph embeddings (node and graph embeddings), node2vec, LINE, NETMF, κλπ.
scalable graph embeddings, nodesketch, nethash, learning to hash, etc
dense subgraph discovery
graph triangulation
graph streams (apps: counting global and local triangles, k-core)
mining special graph types: temporal graphs, probabilistic graphs (clustering), multilayer graphs, hidden graphs
graph kernels
frequent graph pattern mining
graph spectra
graph sketches
graph neural networks
Keywords
complex networks, approximate and randomized algorithms, management, knowledge discovery
Educational Material Types
- Slide presentations
- Book
- scientific papers
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Reading Assigment | 100 | ✓ | ✓ | ||
| Project | 55 | ✓ | ✓ | ||
| Written assigments | 32 | ✓ | ✓ | ||
| Total | 226 |
Student Assessment
Student Assessment methods
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
- Written Exam with Problem Solving (Summative)
- Report (Formative, Summative)
Bibliography
Additional bibliography for study
- Diane J. Cook (Editor), Lawrence B. Holder (Editor), Mining Graph Data, Wiley, 2006.
- Charu C. Aggarwal, Haixun Wang (Editors), Managing and Mining Graph Data, Springer, 2010.
Last Update
16-11-2022
