Grube, Tim ; Volk, Florian ; Mühlhäuser, Max ; Bhairav, Suhas ; Sachidananda, Vinay ; Elovici, Yuval (2017)
Complexity Reduction in Graphs: A User Centric Approach to Graph Exploration.
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 ; Volk, Florian ; Mühlhäuser, Max ; Bhairav, Suhas ; Sachidananda, Vinay ; Elovici, Yuval |
| Type of entry: | Bibliographie |
| Title: | Complexity Reduction in Graphs: A User Centric Approach to Graph Exploration |
| Language: | German |
| Date: | October 2017 |
| Place of Publication: | Athens, Greece |
| Publisher: | IARIA |
| Book Title: | 10th International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services |
| URL / URN: | http://www.thinkmind.org/download.php?articleid=centric_2017... |
| 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. |
| Uncontrolled Keywords: | - SST - Area Smart Security and Trust;- SSI - Area Secure Smart Infrastructures;SPIN: Smart Protection in Infrastructures and Networks |
| Identification Number: | TUD-CS-2017-0204 |
| 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 |
| Last Modified: | 14 Jun 2021 06:14 |
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