Clustering, visualizing, and navigating for large dynamic graphs

Arnaud Sallaberry 1, * Chris Muelder 2 Kwan-Liu Ma 2
* Auteur correspondant
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : In this paper, we present a new approach to exploring dynamic graphs. We have developed a new clustering algorithm for dynamic graphs which finds an ideal clustering for each time-step and links the clusters together. The resulting time-varying clusters are then used to de- fine two visual representations. The first view is an overview that shows how clusters evolve over time and provides an interface to find and select interesting time-steps. The second view consists of a node link diagram of a selected time-step which uses the clustering to efficiently define the layout. By using the time-dependant clustering, we ensure the stability of our visualization and preserve user mental map by minimizing node motion, while simultaneously producing an ideal layout for each time step. Also, as the clustering is computed ahead of time, the second view updates in linear time which allows for interactivity even for graphs with upwards of tens of thousands of nodes.
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Communication dans un congrès
W. Didimo; M. Patrignani. GD: Graph Drawing, Sep 2012, Redmond, WA, United States. Springer-Verlag Berlin Heidelberg, 20th International Symposium on Graph Drawing, LNCS (7704), pp.487-498, 2013, Revised Selected Papers. 〈10.1007/978-3-642-36763-2_43〉
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Dernière modification le : jeudi 24 mai 2018 - 15:59:25
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Arnaud Sallaberry, Chris Muelder, Kwan-Liu Ma. Clustering, visualizing, and navigating for large dynamic graphs. W. Didimo; M. Patrignani. GD: Graph Drawing, Sep 2012, Redmond, WA, United States. Springer-Verlag Berlin Heidelberg, 20th International Symposium on Graph Drawing, LNCS (7704), pp.487-498, 2013, Revised Selected Papers. 〈10.1007/978-3-642-36763-2_43〉. 〈hal-00736038〉

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