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Improved Cluster Tracking for Visualization of Large Dynamic Graphs

Abstract : Analysis and visualization of dynamic graphs is a challenging problem. Clustering can be applied to dynamic graphs in order to generate interactive visualizations with both high stability and good layout quality. However, the existing implementation is naïve and unoptimized. Here we present new algorithms to improve both the temporal clustering results and the efficiency of the cluster tracking calculation, and evaluate the results and performance.
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Submitted on : Friday, September 30, 2016 - 12:13:30 PM
Last modification on : Friday, October 22, 2021 - 3:07:40 PM
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  • HAL Id : lirmm-00798064, version 1


Chris Muelder, Arnaud Sallaberry, Kwan-Liu Ma. Improved Cluster Tracking for Visualization of Large Dynamic Graphs. EGC: Extraction et Gestion des Connaissances, Jan 2013, Toulouse, France. pp.21-32. ⟨lirmm-00798064⟩



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