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master's thesis
Duboko učenje nad dinamičkim grafovima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:926296
Vusić, Daniel
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Vusić, D. (2022). Duboko učenje nad dinamičkim grafovima (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:926296

Vusić, Daniel. "Duboko učenje nad dinamičkim grafovima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:926296

Vusić, Daniel. "Duboko učenje nad dinamičkim grafovima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:926296

Vusić, D. (2022). 'Duboko učenje nad dinamičkim grafovima', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 22 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:926296

Vusić D. Duboko učenje nad dinamičkim grafovima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 June 22] Available at: https://urn.nsk.hr/urn:nbn:hr:168:926296

D. Vusić, "Duboko učenje nad dinamičkim grafovima", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:926296

Metadata
TitleDuboko učenje nad dinamičkim grafovima
Title (english)Deep learning on dynamic graphs
AuthorDaniel Vusić
MentorBoris Vrdoljak (mentor)
Committee memberBoris Vrdoljak (predsjednik povjerenstva)
Committee memberMirta Baranović (član povjerenstva)
Committee memberMario Brčić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-05, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Duboko učenje nad dinamičkim grafovima uključuje razne načine rješavanja zadataka koji su postavljeni nad skupom podataka predstavljenim grafom u kontinuiranom vremenu. Klasifikacija čvorova, predikcija veza te još neki zadatci sasvim su novi i specifični izazovi koje metode klasičnog dubokog učenja ne mogu riješiti zbog različitosti podataka. Graf osim značajki čvorova sadrži puno više informacija na temelju njegove topologije, te još više informacija kada se uzme u obzir kako se taj graf oblikovao kroz vrijeme. Modeli dubokog učenja nad grafovima raznim tehnikama koriste upravo te informacije za dobivanje najboljih rezultata.
Abstract (english)
Deep learning over dynamic graphs involves various ways of solving tasks that are set over a set of data represented by a graph in continuous time. Node classification, link prediction and few other tasks are completely new and specific challenges that the methods of classical deep learning cannot solve due to the diversity of data. In addition to the node features, the graph contains much more information based on its topology, and even more information when you consider how that graph was formed over time. Deep learning models of graphs using various techniques use this information to get the best results.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:926296
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 on2023-01-14 10:16:51