Interconnection: from Dynamic Graphs to Social Networks WM-I-IDG-EN

The aim of this course is to provide students some knowledge about dynamic networks. They will learn how to generate such networks, how to design and implement processes on them and how to perform relevant statistical measures.
A dynamic graph library (graph stream) is used for ease the implementation of algorithms in java.

(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się

information and communication technology

(in Polish) E-Learning

Term 2023/24_Z:

(in Polish) E-Learning

Term 2024/25_Z:

(in Polish) E-Learning

Term 2021/22_Z:

(in Polish) E-Learning (pełny kurs) z podziałem na grupy

(in Polish) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

(in Polish) Opis nakładu pracy studenta w ECTS

LECTURE: Estimated student workload: - participation in classes: 30h - consultation with lecturer: 5h - independent reading: 10h - preparation of the project: 30h 75h in total corresponding to 3 ECTS LABORATORY: Estimated student workload: - participation in classes: 30h - consultation with lecturer: 5h - homework: 20h - preparation of the project: 20h 75h in total corresponding to 3 ECTS

Subject level

advanced

Learning outcome code/codes

LECTURES I2_W10, I2_U09 LABORATORIES I2_W10, I2_U09

Type of subject

Term 2023/24_Z:

optional with limited choices

Term 2024/25_Z:

obligatory

Term 2021/22_Z:

obligatory

Preliminary Requirements

graph theory would be a plus java programming would be a plus

Course coordinators

Term 2023/24_Z:

Dorota Dąbrowska
Frederic Guinand

Term 2024/25_Z:

Frederic Guinand

Term 2021/22_Z:

Frederic Guinand

Learning outcomes

LECTURE
Student:
W1: knows how to model complex systems as dynamic graphs,
W2: knows the generic temporal network model,
W3: knows the metrology related to dynamic graphs and can explain results,
W4: knows the main problems related to dynamic graphs and networks,
U1: can design and implement algorithms operating on dynamic graphs.

LABORATORIES
Student:
U1: can use a java library for creating dynamic graphs,
U2: can design and implement specific algorithms for dynamic graphs,
U3: can design and implement mobility models for generating dynamic networks,
W1: knows the main relevant measures for complex and temporal networks.

Assessment criteria

For all learning outcomes, the following assessment criteria are adopted for all forms of verification: grade 5: fully achieved (no obvious shortcomings),
grade 4.5: achieved almost fully and criteria for awarding a higher grade are not met,
grade 4: largely achieved and the criteria for a higher grade are not met,
grade 3.5: largely achieved - with a clear majority of positives - and the criteria for granting a higher grade are not met,
grade 3: achieved for most of the cases covered by the verification and criteria for a higher grade are not met,
grade 2: not achieved for most of the cases covered by the verification.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of WM-I-IDG-EN in USOSweb