Interactive Exploration of Large Dynamic Networks
Identifieur interne : 001134 ( Main/Exploration ); précédent : 001133; suivant : 001135Interactive Exploration of Large Dynamic Networks
Auteurs : Mathias Pohl [Allemagne] ; Peter Birke [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2008.
Abstract
Abstract: A variety of applications especially in the area of Web 2.0 produce frequently altering hierarchical networks. Thus application operators, members of community websites but also media scientists often are interested in gaining deeper insights in the complex structures of their project and its development over time. In order to enable a suitable visualization of such networks it is not only required to implement an intelligent data management but also a suitable network drawing engine that cares for the user’s needs – especially for visualization of network dynamics. This paper presents the Xldn visualization tool that enables visual exploration of evolving hierarchical networks. It includes a layout generation algorithm that allows for the preservation of the mental map – a crucial property when visualizing dynamic networks. By utilization of an efficient data management and a parallel implementation of the graph drawing algorithm Xldn provides a reasonably fast and interactive network visualization. This way Xldn facilitates an in-depth evolution analysis of large hierarchic networks.
Url:
DOI: 10.1007/978-3-540-85891-1_8
Affiliations:
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Thus application operators, members of community websites but also media scientists often are interested in gaining deeper insights in the complex structures of their project and its development over time. In order to enable a suitable visualization of such networks it is not only required to implement an intelligent data management but also a suitable network drawing engine that cares for the user’s needs – especially for visualization of network dynamics. This paper presents the Xldn visualization tool that enables visual exploration of evolving hierarchical networks. It includes a layout generation algorithm that allows for the preservation of the mental map – a crucial property when visualizing dynamic networks. By utilization of an efficient data management and a parallel implementation of the graph drawing algorithm Xldn provides a reasonably fast and interactive network visualization. This way Xldn facilitates an in-depth evolution analysis of large hierarchic networks.</div></front></TEI><affiliations><list><country><li>Allemagne</li></country><region><li>Rhénanie-Palatinat</li></region><settlement><li>Trèves (Allemagne)</li></settlement><orgName><li>Université de Trèves</li></orgName></list><tree><country name="Allemagne"><region name="Rhénanie-Palatinat"><name sortKey="Pohl, Mathias" sort="Pohl, Mathias" uniqKey="Pohl M" first="Mathias" last="Pohl">Mathias Pohl</name></region><name sortKey="Birke, Peter" sort="Birke, Peter" uniqKey="Birke P" first="Peter" last="Birke">Peter Birke</name><name sortKey="Birke, Peter" sort="Birke, Peter" uniqKey="Birke P" first="Peter" last="Birke">Peter Birke</name><name sortKey="Pohl, Mathias" sort="Pohl, Mathias" uniqKey="Pohl M" first="Mathias" last="Pohl">Mathias Pohl</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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