| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Show document Help

Title:Modeliranje rastočega omrežja z Barabási–Albertovim modelom : na študijskem programu Predmetni učitelj, usmeritev izobraževalna matematika
Authors:ID Bezjak, Katja (Author)
ID Dravec, Tanja (Mentor) More about this mentor... New window
ID Taranenko, Andrej (Comentor)
Files:.pdf EMAG_Bezjak_Katja_2026.pdf (28,50 MB)
MD5: E635F2F2E2A0C77E666C026503B8B413
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FNM - Faculty of Natural Sciences and Mathematics
Abstract:V magistrskem delu obravnavamo problem modeliranja in analize rastočih kompleksnih omrežij, ki se pojavljajo v številnih realnih sistemih. Poseben poudarek je namenjen omrežjem citiranja znanstvenih člankov, saj ta predstavljajo pomemben vir informacij o razvoju in širjenju znanja na posameznem raziskovalnem področju. V delu predstavimo osnovne pojme teorije grafov, verjetnosti in kompleksnih omrežij ter najpomembnejše mere centralnosti, s katerimi vrednotimo pomembnost posameznih vozlišč. Opišemo uporabljena orodja in metode za pridobivanje ter obdelavo podatkov. Nato analiziramo izbrano realno omrežje citatov, pri čemer se osredotočimo na njegove strukturne lastnosti, porazdelitev stopenj vozlišč in rast skozi čas. Predstavimo konstrukcijo sintetičnega omrežja po Barabási–Albertovem modelu in primerjamo lastnosti realnega in sintetičnega omrežja. Končna ugotovitev kaže, da Barabási–Albertov model v veliki meri ustrezno opisuje rast in osnovne strukturne značilnosti analiziranega citatnega omrežja, zlasti pojav preferenčne vezave in neenakomerno porazdelitev stopenj vozlišč. Kljub temu se v nekaterih lastnostih pojavijo razlike med realnim in sintetičnim omrežjem, kar kaže na vpliv dodatnih dejavnikov v realnih sistemih, kot so vsebinska povezanost člankov, raziskovalni trendi in časovna dinamika objavljanja, ki jih model ne zajame v celoti.
Keywords:Kompleksna omrežja, rastoča omrežja, omrežja citatov, Barabási–Albertov model, teorija grafov, mere centralnosti.
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[K. Bezjak]
Year of publishing:2026
Number of pages:X, 60 f.
PID:20.500.12556/DKUM-97839 New window
UDC:519.17(043.2)
COBISS.SI-ID:279503363 New window
Publication date in DKUM:27.05.2026
Views:248
Downloads:19
Metadata:XML DC-XML DC-RDF
Categories:FNM
:
Copy citation
  
Average score:(0 votes)
Your score:Voting is allowed only for logged in users.
Share:Bookmark and Share



Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:17.04.2026

Secondary language

Language:English
Title:Modeling a growing network with the Barabási–Albert model : magistrsko delo
Abstract:In this master’s thesis, we address the problem of modeling and analyzing growing complex networks that appear in many real-world systems. Special emphasis is placed on citation networks of scientific articles, as they represent an important source of information about the development and dissemination of knowledge in a specific research field. We present the basic concepts of graph theory, probability, and complex networks, as well as the most important centrality measures used to evaluate the importance of individual vertices. We also describe the tools and methods used for data acquisition and processing. We then analyze a selected real citation network, focusing on its structural properties, degree distribution, and temporal growth. We present the construction of a synthetic network based on the Barabási–Albert model and compare the properties of the real and synthetic networks. The final results show that the Barabási–Albert model largely provides an adequate description of the growth and basic structural characteristics of the analyzed citation network, particularly the phenomenon of preferential attachment and the irregular degree distribution. Nevertheless, certain differences between the real and synthetic networks indicate the influence of additional factors in real systems, such as the thematic relatedness of articles, research trends, and the temporal dynamics of publishing, which are not fully captured by the model.
Keywords:Complex networks, growing networks, citation networks, Barabási–Albert model, graph theory, centrality measures.


Comments

Leave comment

You must log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica