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National Repository of Grey Literature 1 records found  Search took 0.01 seconds. 
Graph data analysis using deep learning methods
 Vancák, Vladislav ; Svoboda, Martin (advisor) ; Majerech, Vladan (referee)
The goal of this thesis is to investigate the existing graph embedding methods. We aim to represent the nodes of undirected weighted graphs as low-dimensional vectors, also called embeddings, in order to create a rep- resentation suitable for various analytical tasks such as link prediction and clustering. We first introduce several contemporary approaches allowing to create such network embeddings. We then propose a set of modifications and improvements and assess the performance of the enhanced models. Finally, we present a set of evaluation metrics and use them to experimentally evalu- ate and compare the presented techniques on a series of tasks such as graph visualisation and graph reconstruction. 1
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