Clustering, Visualizing, and Navigating for Large Dynamic Graphs

Sallaberry, Arnaud and Muelder, Chris and Ma, Kwan-Liu (2013) Clustering, Visualizing, and Navigating for Large Dynamic Graphs. In: 20th International Symposium, GD 2012, September 19-21, 2012, Redmond, WA, USA , pp. 487-498 (Official URL: http://link.springer.com/chapter/10.1007/978-3-642-36763-2_43).

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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 define 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.

Item Type:Conference Paper
Additional Information:10.1007/978-3-642-36763-2_43
Classifications:M Methods > M.300 Dynamic / Incremental / Online
P Styles > P.720 Straight-line
P Styles > P.999 Others
S Software and Systems > S.120 Visualization
ID Code:1336

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