Network Theory
| Title | Θεωρία Δικτύων / Network Theory |
| Code | 045 |
| Faculty | Engineering |
| School | Electrical and Computer Engineering |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Winter |
| Coordinator | Leonidas Pitsoulis |
| Common | No |
| Status | Active |
| Course ID | 600000994 |
Programme of Study: Electrical and Computer Engineering
Registered students: 132
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| ELECTRICAL ENERGY | Elective Courses | 7 | 4 | 4 |
| ELECTRONICS AND COMPUTER ENGINEERING | Elective Courses | 7 | 4 | 4 |
| TELECOMMUNICATIONS | Elective Courses | 7 | 4 | 4 |
| Academic Year | 2021 – 2022 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 4 |
| Class ID | 600196743
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600196743
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
1. Linear Algebra
2. Probability Theory
3. Programming
Learning Outcomes
The objective of the course is to offer a broad introduction to the theory and algorithms of complex networks. Upon completion the student should be able to:
1. be familiar with the different types of networks such as technological, social, biological and information networks.
2. be knowledgeable about the fundamental network theory, such as graph theoretical concepts, measures of importance and metrics and large-scale structural characteristics of networks.
3. apply basic network related algorithms such as shortest paths and search, maximum flows, partitioning, community detection and spectral methods.
4. be familiar with various random graph models and their characteristics, as well as models for network formation and processes on networks.
General Competences
- Work autonomously
- Advance free, creative and causative thinking
Course Content (Syllabus)
- Real life networks, technological, biological, social and networks of information.
- Graph theory concepts, degree sequences, weighted and directed graphs, trees, connectivity, duality, paths, hypergraphs, signed graphs.
- Node centrality measures and metrics, degree centrality, PageRank, closeness and betweenness, distance, network diameter, hubs.
- Structural properties, large-scale networks, giant components, degree distributions, power law and scale-free networks, clustering coefficient.
- Network algorithms, data structures for network representation, shortest paths, flows, graph partitioning, community detection.
- Network models, random graphs, Erdos-Renyi model, power-law degree distribution, Barabasi-Albert model for network formation, small-world model.
- Processes on networks, percolation, epidemics and spread of information, dynamics in networks.
- Visualization, algorithms, graph formats and repositories, packages and libraries.
Educational Material Types
- Notes
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Communication with Students
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 45 | 1.5 | |||
| Tutorial | 45 | 1.5 | |||
| Exams | 30 | 1 | |||
| Total | 120 | 4 |
Student Assessment
Description
1. Written Examination of 180 minutes (max)
2. Assessment of optional open problems and projects
Student Assessment methods
- Written Assignment (Summative)
- Written Exam with Problem Solving (Summative)
Bibliography
Course Bibliography (Eudoxus)
1. Networks: An Introduction, M.E.J. Newman, Oxford (2010)
2. Networks, Crowds and Markets: Reasoning about a Highly Connected World, D. Easley and J. Kleinberg, Cambridge (2010)
Last Update
23-12-2015
