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dc.contributor.authorPichugina, Oksana
dc.date.accessioned2015-10-16T15:27:27Z
dc.date.available2015-10-16T15:27:27Z
dc.identifier.urihttp://hdl.handle.net/10464/7324
dc.description.abstractIn the scope of the current thesis we review and analyse networks that are formed by nodes with several attributes. We suppose that different layers of communities are embedded in such networks, besides each of the layers is connected with nodes' attributes. For example, examine one of a variety of online social networks: an user participates in a plurality of different groups/communities – schoolfellows, colleagues, clients, etc. We introduce a detection algorithm for the above-mentioned communities. Normally the result of the detection is the community supplemented just by the most dominant attribute, disregarding others. We propose an algorithm that bypasses dominant communities and detects communities which are formed by other nodes' attributes. We also review formation models of the attributed networks and present a Human Communication Network (HCN) model. We introduce a High School Texting Network (HSTN) and examine our methods for that network.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectAttributed Social Networks, Community Detectionen_US
dc.titleCommunity Detection in Multi-Layer Networksen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Mathematics and Statisticsen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Mathematicsen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-08-01T02:07:49Z


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