Module also offered within study programmes:
General information:
Name:
Networks: Models and Computation
Course of study:
2019/2020
Code:
ZSDA-3-0127-s
Faculty of:
Szkoła Doktorska AGH
Study level:
Third-cycle studies
Specialty:
-
Field of study:
Szkoła Doktorska AGH
Semester:
0
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
Koźlak Jarosław (kozlak@agh.edu.pl)
Dyscypliny:
Moduł multidyscyplinarny
Module summary

A student should know and understand the basic types of networks, their properties and fields of application as well as selected algorithms operating on them. He/she should be get to know in more detail some of the problems that can be solved with the help of network-based models.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence: is able to
M_K001 The student can to work in a group to prepare a scientific study and presentation fulfilling the tasks assigned to him/her and ensure a successful final result of the whole work. SDA3A_K01 Scientific paper,
Presentation
Skills: he can
M_U001 The student is able to understand and analyze research papers about selected works on network models and algorithms operating on them. SDA3A_U02 Scientific paper,
Presentation,
Activity during classes
Knowledge: he knows and understands
M_W001 The student knows and understands the basic network models and selected algorithms operating on networks. SDA3A_W01 Involvement in teamwork,
Scientific paper,
Presentation,
Activity during classes
M_W002 The student knows the main research directions conducted in the field of network models and calculations using such models .. SDA3A_W02 Scientific paper,
Project
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
32 16 0 0 0 0 16 0 0 0 0 0
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Social competence
M_K001 The student can to work in a group to prepare a scientific study and presentation fulfilling the tasks assigned to him/her and ensure a successful final result of the whole work. - - - - - + - - - - -
Skills
M_U001 The student is able to understand and analyze research papers about selected works on network models and algorithms operating on them. - - - - - + - - - - -
Knowledge
M_W001 The student knows and understands the basic network models and selected algorithms operating on networks. + - - - - - - - - - -
M_W002 The student knows the main research directions conducted in the field of network models and calculations using such models .. + - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 64 h
Module ECTS credits 3 ECTS
Udział w zajęciach dydaktycznych/praktyka 32 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 16 h
Realization of independently performed tasks 16 h
Module content
Lectures (16h):

1. Kinds of networks. Technological networks. Social networks. Biological networks. Networks of information.
2. Fundamentals of network theory: Mathematics, measures and metrics, structure.
3. Network models. Random Graphs. Small-world model. Power Laws. Rich-Get-Richer. Long Tail. Models of network formation.
4. Surrounding Contexts of Networks: Homophily. Link formation. Link prediction. Network Robustness. Percolation theory.
5. Relationships. Balanced Networks. Positive and Negative Relationships. Signed networks.
6. Community detection, Hierarchical Clustering. Modularity. Overlapping Communities.
7. Identification of node roles in networks. Transitions between roles. Prediction of roles.
8. Graph matching. Exact matching. Inexact matching. Bounded simulation. MAG. TALE.
9. Identification of frequent patterns and anomalies in dynamic networks. Static graphs – methods based on: analysis of features of nodes, analysis of neighbourhoods of nodes, analysis of global features of graphs. Dynamics graphs – methods based on similarities of graphs, finding typical behaviour, observation of groups of nodes.
10. Spreading Phenomena. Propagation of information and disease in networks. Susceptible-Infected, Susceptible-Infected-Susceptible, and Susceptible-Infected-Recovered Epidemic Models. Epidemic prediction. Key nodes in networks.
11. Network Games. Congestion games and resource pricing. Cooperation in network games. Bayesian games.
12. Selected applications: Analysis of Social Media. Study of Elections, Public Opinion, and Representation. Analysis of Political Events.

Seminar classes (16h):

Students prepare in groups presentations and lectures about topics given by the teacher These topics may concern, but are not limited to following problems:

  • network representation and network state
  • methods for identifying of groups / communities in networks
  • network matching algorithms
  • algorithms for identifying patterns and anomalies in evolving networks
  • models of information propagation in networks

Additional information
Teaching methods and techniques:
  • Lectures: Lecture using typical methods (projector, slides) and multimedia demonstrations of selected simulations.
  • Seminar classes: Preparation of presentations and papers in groups by students. Joint discussion of the problems presented and the results obtained.
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

Calculation of final grade is based on the quality of the prepared presentation and scientific paper with the
overview of the given problem domain, participation in discussions and attendance at seminars and lectures.

The following elements will be taken into account when assessing the presentation and the paper:

  • size of the thematic scope learned
  • ordering the information presented and clarity of the presentation
  • involvement in the student in learning sources related to the topic exceeding thesources directly indicated by the teacher
  • editorial quality of the presentation and the paper
  • quality of the presentation
Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Students enrolled in the module can participate in the classes.
  • Seminar classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Students enrolled in the module can participate in the classes.
Method of calculating the final grade:

The final grade is calculated according to study regulations, taking into account the points aquired for the presentation and the paper, which are modified by indicators calculated on the basis of activity in discussions and the level of attendance at lectures.

Basic assessment (for each of the following components a student can get up to 20 points is calculated considering following aspects of the work:

  • size of the thematic scope learned
  • ordering the information presented and clarity of the presentation
  • involvement of the student in learning sources related to the topic exceeding the sources directly indicated by the teacher
  • editorial quality of the presentation and the paper
  • quality of the presentation

For high attendance at lectures a student can get additional 10 points, and for a high activity during the seminar additional 20 points.
Points obtained are converted into percentages and the final grade is calculated according to thresholds of percentages specified in the study regulations for given grades.

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

Backlogs resulting from absences from classes can be made up by preparing papers on additional topics given during the consultations.

Prerequisites and additional requirements:

Prerequisites and additional requirements not specified

Recommended literature and teaching resources:
  1. David Easley, Jon Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World 1st Edition, Publisher: Cambridge University Press; 1 edition (July 19, 2010)
  2. Albert-László Barabási, Márton Pósfai, Network Science, Cambridge University Press; 1 edition (August 5, 2016)
  3. Mark Newman, Networks: An Introduction, Oxford University Press; 1 edition (May 20, 2010)
Scientific publications of module course instructors related to the topic of the module:
  1. Lukasz Oliwa, Jaroslaw Kozlak: Anomaly detection in dynamic social networks for identifying key events. BESC 2017: 1-6
  2. Bogdan Gliwa, Jaroslaw Kozlak, Anna Zygmunt, Yves Demazeau:
    Combining Agent-Based and Social Network Analysis Approaches to Recognition of Role Influence in Social Media. PAAMS 2016: 109-120
  3. Stanislaw Saganowski, Bogdan Gliwa, Piotr Bródka, Anna Zygmunt, Przemyslaw Kazienko, Jaroslaw Kozlak: Predicting Community Evolution in Social Networks. Entropy 17(5): 3053-3096 (2015)
  4. Anna Zygmunt, Piotr Bródka, Przemyslaw Kazienko, Jaroslaw Kozlak:
    Key Person Analysis in Social Communities within the Blogosphere. J. UCS 18(4): 577-597 (2012)
  5. Bogdan Gliwa, Jaroslaw Kozlak, Anna Zygmunt, Krzysztof Cetnarowicz:
    Models of Social Groups in Blogosphere Based on Information about Comment Addressees and Sentiments. SocInfo 2012: 475-488
  6. Anna Zygmunt, Jaroslaw Kozlak, Bogdan Gliwa: Roles in Local Communities and Global Position in Social Media. ASONAM 2018: 1204-1211
  7. Jaroslaw Kozlak, Anna Zygmunt, Bogdan Gliwa, Krzysztof Rudek:
    Dynamics of Social Roles in the Context of Group Evolution in the Blogosphere. BESC 2018: 179-184
  8. Krzysztof Rudek, Jaroslaw Kozlak: Identification of Patterns in Blogosphere Considering Social Positions of Users and Reciprocity of Relations. HAIS 2018: 108-119
Additional information:

None