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undergraduate thesis
Primjena teorije grafova u analizi društvenih mreža
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:693581
Iveković, David
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Iveković, D. (2023). Primjena teorije grafova u analizi društvenih mreža (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:693581

Iveković, David. "Primjena teorije grafova u analizi društvenih mreža." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:693581

Iveković, David. "Primjena teorije grafova u analizi društvenih mreža." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:693581

Iveković, D. (2023). 'Primjena teorije grafova u analizi društvenih mreža', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 28 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:693581

Iveković D. Primjena teorije grafova u analizi društvenih mreža [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 June 28] Available at: https://urn.nsk.hr/urn:nbn:hr:168:693581

D. Iveković, "Primjena teorije grafova u analizi društvenih mreža", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:693581

Metadata
TitlePrimjena teorije grafova u analizi društvenih mreža
Title (english)Application of graph theory in social network analysis
AuthorDavid Iveković
MentorJosipa Pina Milišić (mentor)
Committee memberJosipa Pina Milišić (predsjednik povjerenstva)
Committee memberAndrea Aglić Aljinović (član povjerenstva)
Committee memberLana Horvat Dmitrović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-06-30, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Analiza društvene mreže postupak je koji nam daje bolje razumijevanje određene mrežne strukture te je široko primjenjiv u različitim područjima kao što su marketing, biologija, informacijska sigurnost... Prilikom analize društvene mreže često se javlja problem identifikacije najvažnijih vrhova. U tu svrhu promatrali smo algoritme kao što su centralnost stupnja vrha, centralnost bliskosti, centralnost središnjeg položaja, centralnost svojstvenog vektora te PageRank centralnost. Za svaku centralnost dali smo mane i prednosti te smo došli do zaključka da nam centralnost svojstvenog vektora te PageRank centralnost daju najbolju informaciju o važnosti pojedinog vrha u mreži. U društvenoj mreži vrlo je bitno podijeliti korisnike u neke manje zajednice kako bi obuhvatili ljude sa istim interesima i ponašanjima. Prošli smo kroz algoritme detekcije klika, Louvainov algoritam te Girvan-Newman alogoritam. Nakon što smo usporedili algoritme te istaknuli njihove prednosti i mane mogli smo doći do zaključka da će nam najčešće Louvainov algoritam dati najbolju podjelu mreže na zajednice.
Abstract (english)
Social network analysis is a procedure that gives us a better understanding of a certain network structure and is widely applicable in various fields such as marketing, biology, information security... When analyzing a social network, the problem of identifying the most important nodes often arises. For this purpose, we observed algorithms such as vertex degree centrality, closeness centrality, betweenness centrality, eigenvector centrality and PageRank centrality. For each centrality, we gave disadvantages and advantages, and we came to the conclusion that the centrality of the eigenvector and the PageRank centrality give us the best information about the importance of an individual vertex in the network. In a social network, it is very important to divide users into smaller communities in order to include people with the same interests and behaviors. We went through clique detection algorithm, the Louvain algorithm and the Girvan-Newman algorithm. After comparing the algorithms and highlighting their advantages and disadvantages, we could come to the conclusion that the Louvain algorithm will most often give us the best division of the network into communities.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:693581
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-07-13 10:07:48