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master's thesis
Analiza velikih mreža zasnovana na strojnom učenju
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. urn:nbn:hr:168:165477
Bukovac, Nikola
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Bukovac, N. (2018). Analiza velikih mreža zasnovana na strojnom učenju (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:165477

Bukovac, Nikola. "Analiza velikih mreža zasnovana na strojnom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:165477

Bukovac, Nikola. "Analiza velikih mreža zasnovana na strojnom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:165477

Bukovac, N. (2018). 'Analiza velikih mreža zasnovana na strojnom učenju', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 26 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:165477

Bukovac N. Analiza velikih mreža zasnovana na strojnom učenju [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 May 26] Available at: https://urn.nsk.hr/urn:nbn:hr:168:165477

N. Bukovac, "Analiza velikih mreža zasnovana na strojnom učenju", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:165477

Metadata
TitleAnaliza velikih mreža zasnovana na strojnom učenju
Title (english)Machine Learning Based Large-Scale Network Analysis
AuthorNikola Bukovac
MentorIgor Čavrak (mentor)
Committee memberIgor Čavrak (predsjednik povjerenstva)
Committee memberDanko Basch (član povjerenstva)
Committee memberIvana Bosnić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-07-19, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Reprezentiranjem čvorova moguće je raditi analizu strojnim učenjem nad velikim mrežama s obzirom na to da je svaki čvor prikazan kao vektor realnih vrijednosti određene veličine koja je mnogo manja od broja čvorova u mreži. Analiza je napravljena korištenjem šest algoritama reprezentativnog učenja čvorova s različitim metodama učenja kako bi se ustanovilo koje su metode najbolje. Izbor najboljeg algoritma za reprezentiranje čvorova je ovisan i o metodi analize koju želimo napraviti nad skupom podataka, ali i o parametrima algoritma koji su odabrani pa je provedena i analiza utjecaja parametara na utjecaj algoritama. Provedene su analize klasifikacijom čvorova, predviđanjem veza, grupiranjem čvorova i vizualizacije čvorova koje mogu imati različita značenja ovisno o domeni primjene.
Abstract (english)
Using node representation learning to create node representations it is possible to analyze large scale networks with machine learning because every node is representated as a vector of real numbers which size is a lot smaller than the number of nodes in the network. Analysis was made using six representation learning algorithms with different learning methods to determine which methods were the best. The choice of the best representation learning algorithm is dependent on the analysis method we want to do on the dataset, but also on the chosen algorithm parameters and because of that a parameter analysis is done. Analyzes were performed by classifying nodes, predicting links, grouping nodes, and visualizing nodes that could have different meanings depending on the application domain.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:165477
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2019-03-13 19:26:14