Focus-based filtering + clustering technique for power-law networks with small world phenomenon

Abstract : Realistic interaction networks usually present two main properties: a power-law degree distribution and a small world behavior. Few nodes are linked to many nodes and adjacent nodes are likely to share common neighbors. Moreover, graph structure usually presents a dense core that is difficult to explore with classical filtering and clustering techniques. In this paper, we propose a new filtering technique accounting for a user-focus. This technique extracts a tree-like graph with also power-law degree distribution and small world behavior. Resulting structure is easily drawn with classical force-directed drawing algorithms. It is also quickly clustered and displayed into a multi-level silhouette tree (MuSi-Tree) from any user-focus. We built a new graph filtering + clustering + drawing API and report a case study.
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Communication dans un congrès
Electronic Imaging, Jan 2006, San Jose, CA, United States. SPIE (6060), pp.60600Q, 2006, Visualization and Data Analysis. 〈10.1117/12.649625〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00128375
Contributeur : Mountaz Hascoët <>
Soumis le : mercredi 31 janvier 2007 - 20:17:20
Dernière modification le : jeudi 11 janvier 2018 - 06:14:31
Document(s) archivé(s) le : vendredi 25 novembre 2016 - 15:00:08

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Mountaz Hascoët, François Boutin, Jérôme Thievre. Focus-based filtering + clustering technique for power-law networks with small world phenomenon. Electronic Imaging, Jan 2006, San Jose, CA, United States. SPIE (6060), pp.60600Q, 2006, Visualization and Data Analysis. 〈10.1117/12.649625〉. 〈lirmm-00128375〉

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