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User-guided Graph Exploration: A Framework for Algorithmic Complexity Reduction in Large Data Sets

Grube, Tim and Volk, Florian and Mühlhäuser, Max and Bhairav, Suhas and Sachidananda, Vinay and Elovici, Yuval (2018):
User-guided Graph Exploration: A Framework for Algorithmic Complexity Reduction in Large Data Sets.
In: International Journal On Advances in Intelligent Systems, IARIA, pp. 68-80, 11, (12), ISSN 1942-2679,
[Online-Edition: http://thinkmind.org/index.php?view=article&articleid=intsys...],
[Article]

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Abstract

Human exploration of large data sets becomes increasingly difficult with growing amounts of data. For this purpose, such data sets are often visualized as large graphs, depicting information and interrelations as interconnected vertices. A visual representation of such large graphs (for example, social networks, collaboration analyses or biological data sets) has to find a trade-off between showing details in a magnified—or zoomed-in—view and the overall graph structure. Showing these two aspects at the same time results in a visual overload that is largely inaccessible to human users. In this article, we augment previous work and present a new approach to address this overload by combining and extending graph-theoretic properties with community detection algorithms. Our non-destructive approach to reducing visual complexity while retaining core properties of the given graph is user-guided and semi-automated. The results yielded by applying our approach to large real-world network data sets reveal a massive reduction of displayed vertices and connections while keeping essential graph structures intact.

Item Type: Article
Erschienen: 2018
Creators: Grube, Tim and Volk, Florian and Mühlhäuser, Max and Bhairav, Suhas and Sachidananda, Vinay and Elovici, Yuval
Title: User-guided Graph Exploration: A Framework for Algorithmic Complexity Reduction in Large Data Sets
Language: English
Abstract:

Human exploration of large data sets becomes increasingly difficult with growing amounts of data. For this purpose, such data sets are often visualized as large graphs, depicting information and interrelations as interconnected vertices. A visual representation of such large graphs (for example, social networks, collaboration analyses or biological data sets) has to find a trade-off between showing details in a magnified—or zoomed-in—view and the overall graph structure. Showing these two aspects at the same time results in a visual overload that is largely inaccessible to human users. In this article, we augment previous work and present a new approach to address this overload by combining and extending graph-theoretic properties with community detection algorithms. Our non-destructive approach to reducing visual complexity while retaining core properties of the given graph is user-guided and semi-automated. The results yielded by applying our approach to large real-world network data sets reveal a massive reduction of displayed vertices and connections while keeping essential graph structures intact.

Journal or Publication Title: International Journal On Advances in Intelligent Systems
Title of Book: International Journal
Volume: 11
Number: 12
Place of Publication: Athens, Greece
Publisher: IARIA
Uncontrolled Keywords: - SST - Area Smart Security and Trust;- SSI - Area Secure Smart Infrastructures;SPIN: Smart Protection in Infrastructures and Networks
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Telecooperation
Date Deposited: 11 Jun 2019 11:50
Official URL: http://thinkmind.org/index.php?view=article&articleid=intsys...
Identification Number: TUD-CS-2017-0204
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