Literature DB >> 26761819

CUBu: Universal Real-Time Bundling for Large Graphs.

Matthew van der Zwan, Valeriu Codreanu, Alexandru Telea.   

Abstract

Visualizing very large graphs by edge bundling is a promising method, yet subject to several challenges: speed, clutter, level-of-detail, and parameter control. We present CUBu, a framework that addresses the above problems in an integrated way. Fully GPU-based, CUBu bundles graphs of up to a million edges at interactive framerates, being over 50 times faster than comparable state-of-the-art methods, and has a simple and intuitive control of bundling parameters. CUBu extends and unifies existing bundling techniques, offering ways to control bundle shapes, separate bundles by edge direction, and shade bundles to create a level-of-detail visualization that shows both the graph core structure and its details. We demonstrate CUBu on several large graphs extracted from real-life application domains.

Year:  2016        PMID: 26761819     DOI: 10.1109/TVCG.2016.2515611

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  3 in total

1.  Multilevel Coarsening for Interactive Visualization of Large Bipartite Networks.

Authors:  Alan Demétrius Baria Valejo; Renato Fabbri; Alneu de Andrade Lopes; Liang Zhao; Maria Cristina Ferreira de Oliveira
Journal:  Front Res Metr Anal       Date:  2022-06-16

2.  Scanpath visualization and comparison using visual aggregation techniques.

Authors:  Vsevolod Peysakhovich; Christophe Hurter
Journal:  J Eye Mov Res       Date:  2018-01-08       Impact factor: 0.957

3.  An Information-Theoretic Framework for Evaluating Edge Bundling Visualization.

Authors:  Jieting Wu; Feiyu Zhu; Xin Liu; Hongfeng Yu
Journal:  Entropy (Basel)       Date:  2018-08-21       Impact factor: 2.524

  3 in total

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