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master's thesis
Klasifikacija čvorova u grafu metodama dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:901283
Filipović, Antonio
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Filipović, A. (2022). Klasifikacija čvorova u grafu metodama dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:901283

Filipović, Antonio. "Klasifikacija čvorova u grafu metodama dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:901283

Filipović, Antonio. "Klasifikacija čvorova u grafu metodama dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:901283

Filipović, A. (2022). 'Klasifikacija čvorova u grafu metodama dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 19 December 2024, https://urn.nsk.hr/urn:nbn:hr:168:901283

Filipović A. Klasifikacija čvorova u grafu metodama dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 December 19] Available at: https://urn.nsk.hr/urn:nbn:hr:168:901283

A. Filipović, "Klasifikacija čvorova u grafu metodama dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:901283

Metadata
TitleKlasifikacija čvorova u grafu metodama dubokog učenja
Title (english)Node Classification in Graphs by Deep Learning Methods
AuthorAntonio Filipović
MentorTomislav Burić (mentor)
Committee memberTomislav Burić (predsjednik povjerenstva)
Committee memberJosipa Pina Milišić (član povjerenstva)
Committee memberMate Puljiz (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-08, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Tema rada je primjena metoda dubokog učenja na klasifikaciju čvorova u grafu. Iz obrade signala matrice susjedstva proizašle su metode dubokog učenja koje imaju veliku primjenu u teoriji grafova i predstavljaju potencijalno veliki skok u uspješnosti rješavanja problema klasifikacije čvorova. Metode dubokog učenja specifično se treniraju samo za određeni zadatak, a prije njih postojale su metode koje koriste jedino strukturu grafa za sva daljna predviđanja. Cilj rada je implementirati i detaljno usporediti metode dubokog učenja (Graph Convolutional Networks, Graph Attention Networks i GraphSAGE) s metodom zasnovanom na strukturi grafa (Node2Vec) u rješavanju problema klasifikacije čvorova.
Abstract (english)
The theme of the master thesis is the application of deep learning methods to node classification in graphs. Most recent deep learning methods on graphs were born from the graph signal processing field and such methods present a potentially giant leap in the advancement of the graph machine learning field. Such methods utilize node features together with graph structure and are trained specifically for the task at the hand. Many deep learning methods before them only use graph structure and are trained for a broader range of problems. The goal of the master thesis is to implement and compare the most recent deep learning methods such as Graph Convolutional Networks, Graph Attention Networks, and GraphSAGE with older methods based only on graph structure such as Node2Vec.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:901283
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
Access conditionsClosed access
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Created on2023-01-13 14:53:22