Real-time Intelligent Clustering for Graph Visualization

Lionel Martin, Géraldine Bous

2013

Abstract

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.

Paper Citation

in Harvard Style

Martin L. and Bous G. (2013). Real-time Intelligent Clustering for Graph Visualization . In Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2013) ISBN 978-989-8565-46-4, pages 471-480. DOI: 10.5220/0004305504710480

in Bibtex Style

@conference{ivapp13,
author={Lionel Martin and Géraldine Bous},
title={Real-time Intelligent Clustering for Graph Visualization},
booktitle={Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2013)},
year={2013},
pages={471-480},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004305504710480},
isbn={978-989-8565-46-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2013)
TI - Real-time Intelligent Clustering for Graph Visualization
SN - 978-989-8565-46-4
AU - Martin L.
AU - Bous G.
PY - 2013
SP - 471
EP - 480
DO - 10.5220/0004305504710480