Real-Time Intelligent Clustering for Graph Visualization

We present a tool for the interactive exploration and analysis of large clustered graphs. The tool empowers users to control the granularity of the graph, either by direct interaction (collapsing/expanding clusters) or via a slider that automatically computes a clustered graph of the desired size. Moreover, we explore the use of learning algorithms to capture graph exploration preferences based on a history of user interactions. The learned parameters are then used to modify the action of the slider in view of mimicking the natural interaction/exploration behavior of the user.


Published in:
Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications, 471-480
Presented at:
8th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Barcelona, Spain, February 21-24, 2013
Year:
2013
Publisher:
SciTePress
Keywords:
Laboratories:


Note: The status of this file is: EPFL only


 Record created 2013-10-01, last modified 2018-03-17

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