Finding Collections of k-Clique Percolated Components in Attributed Graphs

Pierre-Nicolas Mougel 1, 2 Christophe Rigotti 1, 2, 3, * Olivier Gandrillon 4, 5
* Auteur correspondant
2 BEAGLE - Artificial Evolution and Computational Biology
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive, CarMeN - Laboratoire de recherche en cardiovasculaire, métabolisme, diabétologie et nutrition
3 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
4 DRACULA - Multi-scale modelling of cell dynamics : application to hematopoiesis
CGMC - Centre de génétique moléculaire et cellulaire, Inria Grenoble - Rhône-Alpes, ICJ - Institut Camille Jordan [Villeurbanne], UCBL - Université Claude Bernard Lyon 1 : EA
Abstract : In this paper, we consider graphs where a set of Boolean attributes is associated to each vertex, and we are interested in k-clique percolated components (components made of overlapping cliques) in such graphs. We propose the task of finding the collections of homogeneous k-clique percolated components, where homogeneity means sharing a common set of attributes having value true. A sound and complete algorithm based on subgraph enumeration is proposed. We report experiments on two real databases (a social network of scientific collaborations and a network of gene interactions), showing that the extracted patterns capture meaningful structures.
Type de document :
Communication dans un congrès
Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2012, Kuala Lumpur, Malaysia. 7302, pp.181--192, 2012, LNCS
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https://hal.inria.fr/hal-00758843
Contributeur : Christophe Rigotti <>
Soumis le : jeudi 29 novembre 2012 - 14:16:25
Dernière modification le : mardi 16 janvier 2018 - 16:04:55

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  • HAL Id : hal-00758843, version 1

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Pierre-Nicolas Mougel, Christophe Rigotti, Olivier Gandrillon. Finding Collections of k-Clique Percolated Components in Attributed Graphs. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2012, Kuala Lumpur, Malaysia. 7302, pp.181--192, 2012, LNCS. 〈hal-00758843〉

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