Graph visualization with latent variable models

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorNybo, Kristian
dc.contributor.authorParkkinen, Juuso
dc.contributor.authorKaski, Samuel
dc.contributor.departmentDepartment of Information and Computer Scienceen
dc.contributor.departmentTietojenkäsittelytieteen laitosfi
dc.contributor.schoolFaculty of Information and Natural Sciencesen
dc.contributor.schoolInformaatio- ja luonnontieteiden tiedekuntafi
dc.date.accessioned2011-11-28T13:23:48Z
dc.date.available2011-11-28T13:23:48Z
dc.date.issued2009
dc.description.abstractLarge graph layout design by choosing locations for the vertices on the plane, such that the drawn set of edges is understandable, is a tough problem. The goal is ill-defined and usually both optimization and evaluation criteria are only very indirectly related to the goal. We suggest a new and surprisingly effective visualization principle: Position nodes such that nearby nodes have similar link distributions. Since their edges are similar by definition, the edges will become visually bundled and do not interfere. For the definition of similarity we use latent variable models which incorporate the user's assumption of what is important in the graph, and given the similarity construct the visualization with a suitable nonlinear projection method capable of maximizing the precision of the display. We finally show that the method outperforms alternative graph visualization methods empirically, and that at least in the special case of clustered data the method is able to properly abstract and visualize the links.en
dc.format.extent15
dc.format.mimetypeapplication/pdf
dc.identifier.isbn978-952-248-095-5
dc.identifier.issn1797-5042
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/899
dc.identifier.urnurn:nbn:fi:tkk-013045
dc.language.isoenen
dc.publisherHelsinki University of Technologyen
dc.publisherTeknillinen korkeakoulufi
dc.relation.ispartofseriesTKK reports in information and computer scienceen
dc.relation.ispartofseries20en
dc.subject.keywordgraph clusteringen
dc.subject.keywordgraph visualizationen
dc.subject.keywordlatent variable modelen
dc.subject.otherComputer scienceen
dc.titleGraph visualization with latent variable modelsen
dc.typeD4 Julkaistu kehittämis- tai tutkimusraportti taikka -selvitysfi
dc.type.dcmitypetexten
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