Drawing Large Graphs by Multilevel Maxent-Stress Optimization

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 the maxent-stress metric proposed by Gansner et al. (2013) that combines layout stress and entropy....

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 24; no. 5; pp. 1814 - 1827
Main Authors: Meyerhenke, Henning, Nollenburg, Martin, Schulz, Christian
Format: Journal Article
Language:English
Published: United States IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
algorithm engineering
Approximation algorithms
Computational modeling
Force
Graph layout
Graphs
Iterative methods
Layout
Layouts
Linear systems
Local optimization
Multilevel
multilevel algorithm
Optimization
Stress
ISSN:1077-2626, 1941-0506, 1941-0506
Online Access:Get full text
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Summary: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 the maxent-stress metric proposed by Gansner et al. (2013) that combines layout stress and entropy. As opposed to previous work, we do not solve the resulting 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 a single thread) while producing a comparable solution quality.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2017.2689016