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Complexity Reduction in Graphs: A User Centric Approach to Graph Exploration

Grube, Tim and Volk, Florian and Mühlhäuser, Max and Bhairav, Suhas and Sachidananda, Vinay and Elovici, Yuval (2017):
Complexity Reduction in Graphs: A User Centric Approach to Graph Exploration.
In: 10th International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services, Athens, Greece, IARIA, [Online-Edition: http://www.thinkmind.org/download.php?articleid=centric_2017...],
[Conference or Workshop Item]

Abstract

Human exploration of large graph structures becomes increasingly difficult with growing graph sizes. A visual representation of such large graphs, for example, social networks and citational networks, has to find a trade-off between showing details in a magnified view and the verall graph structure. Displaying these both aspects at the same time results in an overloaded visualization that is inaccessible for human users. In this paper, we present a new approach to address this issue by combining and extending graph-theoretic properties with community detection algorithms. Our approach is semi-automated and non-destructive. The aim is to retain core properties of the graph while--at the same time--hiding less important side information from the human user. We analyze the results yielded by applying our approach to large real-world network data sets, revealing a massive reduction of displayed nodes and links.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Grube, Tim and Volk, Florian and Mühlhäuser, Max and Bhairav, Suhas and Sachidananda, Vinay and Elovici, Yuval
Title: Complexity Reduction in Graphs: A User Centric Approach to Graph Exploration
Language: German
Abstract:

Human exploration of large graph structures becomes increasingly difficult with growing graph sizes. A visual representation of such large graphs, for example, social networks and citational networks, has to find a trade-off between showing details in a magnified view and the verall graph structure. Displaying these both aspects at the same time results in an overloaded visualization that is inaccessible for human users. In this paper, we present a new approach to address this issue by combining and extending graph-theoretic properties with community detection algorithms. Our approach is semi-automated and non-destructive. The aim is to retain core properties of the graph while--at the same time--hiding less important side information from the human user. We analyze the results yielded by applying our approach to large real-world network data sets, revealing a massive reduction of displayed nodes and links.

Title of Book: 10th International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services
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
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 2050 Privacy and Trust for Mobile Users
Profile Areas
Profile Areas > Cybersecurity (CYSEC)
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > CRISP - Center for Research in Security and Privacy
Date Deposited: 28 Jul 2017 13:33
Official URL: http://www.thinkmind.org/download.php?articleid=centric_2017...
Identification Number: TUD-CS-2017-0204
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