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master's thesis
Detekcija anomalija u grafovima
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. urn:nbn:hr:168:362565
Galić, Dominik
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Galić, D. (2021). Detekcija anomalija u grafovima (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:362565

Galić, Dominik. "Detekcija anomalija u grafovima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:362565

Galić, Dominik. "Detekcija anomalija u grafovima." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:362565

Galić, D. (2021). 'Detekcija anomalija u grafovima', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 16 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:362565

Galić D. Detekcija anomalija u grafovima [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2024 June 16] Available at: https://urn.nsk.hr/urn:nbn:hr:168:362565

D. Galić, "Detekcija anomalija u grafovima", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:362565

Metadata
TitleDetekcija anomalija u grafovima
Title (english)Anomaly detection in graphs
AuthorDominik Galić
MentorBoris Vrdoljak (mentor)
Committee memberBoris Vrdoljak (predsjednik povjerenstva)
Committee memberMario Brčić (član povjerenstva)
Committee memberMirta Baranović (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-07-06, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom diplomskom radu prikazane su vrste grafova s obzirom na vremensku promjenjivost topologije, najčešće vrste anomalija te metode za otkrivanje anomalija u grafovima. Prvi korak je istraživanje postojećih implementacija nad popularnim skupovima podataka koji su javno dostupni. Nakon toga izabrana su dva pristupa za detekciju anomalija u anonimiziranim podacima telekomunikacijske kompanije. Prvi pristup detektirao je anomalije koristeći metodu dubokog strojnog učenja autoencodere, dok se drugi pristup temelji na algoritmu DNODA koji promatra direktno susjedstvo čvorova u grafu. Metode su implementirane korištenjem programskog jezika Python, a vizualizacije bibliotekama za vizualizaciju jezika R.
Abstract (english)
Within this thesis, I have shown different types of graphs based on their topology with respect to the temporal component, the most common types of graph anomalies, and methods for detecting anomalies in graphs. The first step is to investigate existing implementations on popular datasets. The next step is choosing two methods for graph anomaly detection in the anonymized dataset from a telecommunication company. The first approach is detecting anomalies using the deep learning method – autoencoders, and the second approach is based on detecting anomalies with respect to the direct neighborhood of graph nodes. Algorithms were implemented using Python programming language and visualizations are made with programming libraries for the R language.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:362565
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 on2023-01-13 14:55:42