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master's thesis
Konvolucijske mreže grafova
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. urn:nbn:hr:168:600153
Grbac, Natalija
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Grbac, N. (2019). Konvolucijske mreže grafova (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:600153

Grbac, Natalija. "Konvolucijske mreže grafova." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:600153

Grbac, Natalija. "Konvolucijske mreže grafova." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2019. https://urn.nsk.hr/urn:nbn:hr:168:600153

Grbac, N. (2019). 'Konvolucijske mreže grafova', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 28 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:600153

Grbac N. Konvolucijske mreže grafova [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2019 [cited 2024 June 28] Available at: https://urn.nsk.hr/urn:nbn:hr:168:600153

N. Grbac, "Konvolucijske mreže grafova", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:168:600153

Metadata
TitleKonvolucijske mreže grafova
Title (english)Graph Convolution Networks
AuthorNatalija Grbac
MentorLana Horvat Dmitrović (mentor)
Committee memberLana Horvat Dmitrović (član povjerenstva)
Committee memberMario Krnić (član povjerenstva)
Committee memberTomislav Burić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2019-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U okviru ovog rada proučit će se područje primjene konvolucijskih mreža grafova te će se prezentirati i usporediti različiti tipovi istih. Usporedba će se provesti prikladnom programskom implementacijom i primjenom na različitim grafovima socijalnih i drugih mreža koji su javno dostupni. Na početku su objašnjeni osnovni principi rada neuronskih mreža te arhitekture konvolucijskih i povratnih neuronskih mreža. U trećem poglavlju je opisan princip rada neuronskih mreža na grafovima koje slijede rekurzivni pristup polunadziranom učenju na grafovima te su prezentirana dva modela neuronskih mreža grafova . U četvrtom poglavlju je objašnjen princip rada konvolucijskih slojeva na podacima koji nisu iz Euklidske domene. Prikazan je razvoj konvolucijskih mreža grafova koje se oslanjaju na spektralnom pristupu konvoluciji na grafovima, te su detaljno opisane operacije konvolucije i sažimanja u tim mrežama. U zadnjem poglavlju su prezentirani rezultati usporedbe performansa opisanih modela konvolucijskih mreža grafova na zadacima klasifikacije na razini čvorova i na razini grafa.
Abstract (english)
The purpose of this paper is to describe various graph convolutional network models and to compare their performance on both node and graph-level classification tasks on different graphs of social and other networks that are publicly available. The second chapter of this paper describes basic principles of neural networks followed by a detailed explanation of convolutional and recurrent neural network architectures. A recursive approach to semi-supervised learning on graphs is explained in chapter 3 titled Graph neural networks. In this chapter two graph neural network models are presented in detail. The fourth chapter explains the principle of the convolution operation on non-Euclidean domains. The development of graph convolutional networks based on the spectral approach to graph convolution is described. For the graph convolutional models presented in this chapter, the convolutional and pooling operations are described. Finally, the classification performance results for the previously described graph convolutional networks are presented.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:600153
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 on2020-01-07 18:12:44