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Louvain-based Multi-level Graph Drawing

DC Field Value Language
dc.contributor.authorHong, Seok-Hee-
dc.contributor.authorEades, Peter-
dc.contributor.authorTorkel, Marnijati-
dc.contributor.authorWood, James-
dc.contributor.authorPark, Kun Soo-
dc.date.accessioned2022-06-24T00:26:07Z-
dc.date.available2022-06-24T00:26:07Z-
dc.date.created2022-05-09-
dc.date.issued2021-04-
dc.identifier.citationIEEE Pacific Visualization Symposium, Vol.2021-April, pp.151-155-
dc.identifier.issn2165-8765-
dc.identifier.urihttps://hdl.handle.net/10371/183748-
dc.description.abstract© 2021 IEEE.The multi-level graph drawing is a popular approach to visualize large and complex graphs. It recursively coarsens a graph and then uncoarsens the drawing using layout refinement. In this paper, we leverage the Louvain community detection algorithm for the multi-level graph drawing paradigm.More specifically, we present the Louvain-based multi-level graph drawing algorithm, and compare with other community detection algorithms such as Label Propagation and Infomap clustering. Experiments show that Louvain-based multi-level algorithm performs best in terms of efficiency (i.e., fastest runtime), while Label Propagation and Infomap-based multi-level algorithms perform better in terms of effectiveness (i.e., better visualization in quality metrics).-
dc.language영어-
dc.publisherIEEE-
dc.titleLouvain-based Multi-level Graph Drawing-
dc.typeArticle-
dc.identifier.doi10.1109/PacificVis52677.2021.00028-
dc.citation.journaltitleIEEE Pacific Visualization Symposium-
dc.identifier.wosid000680829900021-
dc.identifier.scopusid2-s2.0-85107401481-
dc.citation.endpage155-
dc.citation.startpage151-
dc.citation.volume2021-April-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorPark, Kun Soo-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
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