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Title:Detekcija skupnosti v kompleksnih omrežjih : magistrska naloga
Authors:ID Pritržnik, Robi (Author)
ID Lužar, Borut (Mentor) More about this mentor... New window
Files:.pdf MAG_2025_Robi_Pritrznik.pdf (4,19 MB)
MD5: E89A07AF5059AF14AB657C2D04D0F783
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FIŠ - Faculty of Information Studies in Novo mesto
Abstract:V magistrski nalogi obravnavamo detekcijo skupnosti v kompleksnih omrežjih. Medsebojno primerjamo algoritme za detekcijo skupnosti Louvain, Leiden, Label Propagation, Fast Label Propagation, Greedy modularity, Infomap, Walktrap in Girvan-Newman. Osredotočimo se predvsem na primerjavo strukturnih karakteristik skupnosti, ki so rezultat izvedbe algoritmov na realnih omrežjih karate kluba Zachary, slučajnega omrežja Erdős-Rényi, družbenega omrežja X (Twitter), omrežja nevroznanosti, komunikacijskega omrežja EU organizacije in omrežja citiranosti patentov v ZDA. Med drugim ugotovimo, da je hitrost delovanja algoritmov odvisna od velikosti in strukture omrežja. Izkaže se, da je izmed obravnavanih algoritmov za detekcijo skupnosti v velikih omrežjih najbolj primeren algoritem Leiden, v povprečju pa je najhitreje v vseh primerih deloval algoritem Fast Label Propagation.
Keywords:detekcija skupnosti, omrežja in grafi, struktura omrežij, analiza omrežij, kompleksna omrežja
Place of publishing:Novo mesto
Place of performance:Novo mesto
Publisher:R. Pritržnik
Year of publishing:2025
Year of performance:2025
Number of pages:XVII, 103 str.
PID:20.500.12556/ReVIS-12447 New window
COBISS.SI-ID:253447683 New window
UDC:519.17:004(043.2)
Note:Na ov.: Magistrska naloga : študijskega programa druge stopnje;
Publication date in ReVIS:17.10.2025
Views:266
Downloads:20
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Licences

License:CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.

Secondary language

Language:English
Abstract:In this master's thesis, we discuss community detection in complex networks. We compare the community detection algorithms Louvain, Leiden, Label Propagation, Fast Label Propagation, Greedy modularity, Infomap, Walktrap and Girvan-Newman. We focus mainly on comparing the structural characteristics of communities, which are results of implementing the algorithms on real networks of the Zachary karate club, Erdős-Rényi random network, a social network from X (Twitter), a neuroscience network, a communication network of the EU organization and a patent citation network in the USA. We find that the speed of algorithms depends on the size and structure of networks. It turns out that among the considered algorithms for community detection in large networks, the Leiden algorithm is the most suitable, while on average the Fast Label Propagation algorithm performed the fastest in all cases.
Keywords:community detection, networks and graphs, network structure, network analysis, complex networks


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