Metode strojnega učenja za vektorsko vložitev vozlišč grafa : diplomsko delo

Keršič, Vid

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Title:	Metode strojnega učenja za vektorsko vložitev vozlišč grafa : diplomsko delo
Authors:	ID Keršič, Vid (Author) ID Strnad, Damjan (Mentor) More about this mentor... ID Kohek, Štefan (Comentor)
Files:	UN_Kersic_Vid_2020.pdf (1,89 MB) MD5: 6A44979D87449B2411DC753354325F53 PID: 20.500.12556/dkum/9c9b4be9-6f12-4b29-a9e7-d1c22ebdbb42
Language:	Slovenian
Work type:	Bachelor thesis/paper
Typology:	2.11 - Undergraduate Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	Graf je neevklidska podatkovna struktura, ki jo je težko neposredno analizirati z metodami strojnega učenja, ki obdelujejo podatke v vektorski obliki. Zaradi tega so v zadnjih letih postale priljubljene metode strojnega učenja za vektorsko vložitev, ki graf transformirajo v vektorski prostor. V diplomskem delu zgradimo graf iz člankov z angleške Wikipedije s sledenjem vsebovanim hiperpovezavam. Eksperiment izvedemo za filme in glasbene albume. Vozlišča dobljenega grafa vložimo v vektorski prostor, kar nam omogoči učinkovitejšo analizo grafa, pri kateri se osredotočimo na vizualizacijo, podobnost ter klasifikacijo filmov in albumov v žanre. Med seboj primerjamo vložitve metod DeepWalk, node2vec in SDNE. Pri klasifikaciji filmov v povprečju dosežemo 88,5 % točnost, pri albumih pa 89,3 % točnost.
Keywords:	strojno učenje, graf, vložitev vozlišč, naključni sprehod, avtokodirnik
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[V. Keršič]
Year of publishing:	2020
Number of pages:	XII, 42 str.
PID:	20.500.12556/DKUM-77207
UDC:	004.85:004.422.63(043.2)
COBISS.SI-ID:	38602243
NUK URN:	URN:SI:UM:DK:KEG2RGHE
Publication date in DKUM:	04.11.2020
Views:	1003
Downloads:	113
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Link:	http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:	The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:	21.08.2020

Secondary language

Language:	English
Title:	Machine learning methods for vector embedding of graph nodes
Abstract:	A graph is a non-Euclidean data structure, which is hard to analyze directly with machine learning methods that process data in the vector form. Therefore, in the recent years, machine learning methods for vector embedding, which transform graphs into vector space, have gained a lot of traction. In the thesis, we construct a graph from English Wikipedia articles by following contained hyperlinks. We conduct experiments for movies and music albums. We embed the nodes of the obtained graph in a vector space, which allows us to analyze them more efficiently, focusing on visualization, similarity, and classification of movies and albums into genres. We compare the embeddings produced by methods DeepWalk, node2vec, and SDNE. We achieve, on average, the classification accuracy of 88.5 % for movies and 89.3 % for albums.
Keywords:	machine learning, graph, node embeddings, random walk, autoencoder

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