Social networks can be helpful for the analysis of behaviour of people. An existing social network is rarely available, and its nodes and edges have to be inferred from not necessarily graph data. Link prediction can be used to either correct inaccuracies or to forecast links about to appear in the future. In this work, we study the prediction of miss- ing links in a social network inferred from real-world bank data. We review and compare both verified and modern approaches to link prediction. Following the advancements of deep learning in recent years, we primarily focus on graph neural networks, and their ability to scale to large networks. We propose an adjustment to an existing graph neural network method and show that its performance is either comparable with or outperform- ing the original method. The comparison is performed on two social networks inferred from the same data. We show that it is relatively hard to outperform the verified link prediction methods with graph neural networks. 1
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:448563 |
| Date | January 2021 |
| Creators | Měkota, Ondřej |
| Contributors | Holubová, Irena, Peška, Ladislav |
| Source Sets | Czech ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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