Improved Approximation Algorithms for Box Contact Representations
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Final Accepted Manuscript
Author
Bekos, Michael A.van Dijk, Thomas C.
Fink, Martin
Kindermann, Philipp

Kobourov, Stephen
Pupyrev, Sergey
Spoerhase, Joachim
Wolff, Alexander

Affiliation
Department of Computer Science, University of ArizonaIssue Date
2016-01-27
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SpringerCitation
Improved Approximation Algorithms for Box Contact Representations 2016, 77 (3):902 AlgorithmicaJournal
AlgorithmicaRights
© Springer Science+Business Media New York 2016.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
We study the following geometric representation problem: Given a graph whose vertices correspond to axis-aligned rectangles with fixed dimensions, arrange the rectangles without overlaps in the plane such that two rectangles touch if the graph contains an edge between them. This problem is called Contact Representation of Word Networks (Crown) since it formalizes the geometric problem behind drawing word clouds in which semantically related words are close to each other. Crown is known to be NP-hard, and there are approximation algorithms for certain graph classes for the optimization version, Max-Crown, in which realizing each desired adjacency yields a certain profit. We present the first O(1)-approximation algorithm for the general case, when the input is a complete weighted graph, and for the bipartite case. Since the subgraph of realized adjacencies is necessarily planar, we also consider several planar graph classes (namely stars, trees, outerplanar, and planar graphs), improving upon the known results. For some graph classes, we also describe improvements in the unweighted case, where each adjacency yields the same profit. Finally, we show that the problem is APX-complete on bipartite graphs of bounded maximum degree.Note
12 month embargo; First Online: 27 January 2016ISSN
0178-46171432-0541
Version
Final accepted manuscriptSponsors
ESF EuroGIGA project GraDR; NSF [CCF-1115971, DEB 1053573]Additional Links
http://link.springer.com/10.1007/s00453-016-0121-3ae974a485f413a2113503eed53cd6c53
10.1007/s00453-016-0121-3