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Drawing Large Graphs by Multilevel Maxent-Stress Optimization. [Preprint]

Meyerhenke, Henning; Nöllenburg, Martin; Schulz, Christian

Drawing large graphs appropriately is an important step for the visual analysis of data from real-world networks. Here we present a novel multilevel algorithm to compute a graph layout with respect to a recently proposed metric that combines layout stress and entropy. As opposed to previous work, we do not solve the linear systems of the maxent-stress metric with a typical numerical solver. Instead we use a simple local iterative scheme within a multilevel approach. To accelerate local optimization, we approximate long-range forces and use shared-memory parallelism. Our experiments validate the high potential of our approach, which is particularly appealing for dynamic graphs. In comparison to the previously best maxent-stress optimizer, which is sequential, our parallel implementation is on average 30 times faster already for static graphs (and still faster if executed on one thread) while producing a comparable solution quality.

Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Buchaufsatz
Jahr 2015
Sprache Englisch
Identifikator KITopen ID: 1000050655
HGF-Programm 46.12.02; LK 01
Erschienen in arXiv : Computing Research Repository (CoRR)
Seiten arXiv:1506.04383
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