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dc.contributor.authorWu, Hsiang-Yun
dc.contributor.authorNollenburg, Martin
dc.contributor.authorViola, Ivan
dc.date.accessioned2020-11-25T13:05:19Z
dc.date.available2020-11-25T13:05:19Z
dc.date.issued2020
dc.identifier.citationWu, H.-Y., Nollenburg, M., & Viola, I. (2020). Multi-level Area Balancing of Clustered Graphs. IEEE Transactions on Visualization and Computer Graphics, 1–1. doi:10.1109/tvcg.2020.3038154
dc.identifier.issn2160-9306
dc.identifier.doi10.1109/TVCG.2020.3038154
dc.identifier.urihttp://hdl.handle.net/10754/666110
dc.description.abstractWe present a multi-level area balancing technique for laying out clustered graphs to facilitate a comprehensive understanding of the complex relationships that exist in various fields, such as life sciences and sociology. Clustered graphs are often used to model relationships that are accompanied by attribute-based grouping information. Such information is essential for robust data analysis, such as for the study of biological taxonomies or educational backgrounds. Hence, the ability to smartly arrange textual labels and packing graphs within a certain screen space is therefore desired to successfully convey the attribute data . Here we propose to hierarchically partition the input screen space using Voronoi tessellations in multiple levels of detail. In our method, the position of textual labels is guided by the blending of constrained forces and the forces derived from centroidal Voronoi cells. The proposed algorithm considers three main factors: (1) area balancing, (2) schematized space partitioning, and (3) hairball management. We primarily focus on area balancing, which aims to allocate a uniform area for each textual label in the diagram. We achieve this by first untangling a general graph to a clustered graph through textual label duplication, and then coupling with spanning-tree-like visual integration. We illustrate the feasibility of our approach with examples and then evaluate our method by comparing it with well-known conventional approaches and collecting feedback from domain experts.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9262073/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9262073
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectGraph drawing
dc.subjectVoronoi tessellation
dc.subjectmulti-level
dc.subjectspatially-efficient layout
dc.titleMulti-level Area Balancing of Clustered Graphs
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentVisual Computing Center (VCC)
dc.identifier.journalIEEE Transactions on Visualization and Computer Graphics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionInstitute of Visual Computing & Human-Centered Technology, TU Wien, 27259 Vienna, Vienna Austria
dc.contributor.institutionFaculty of Informatics, TU Wien, Vienna, Vienna Austria
dc.identifier.pages1-1
kaust.personViola, Ivan
refterms.dateFOA2020-12-10T12:23:36Z


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