Graph Aggregation: Application to Social Networks

Abstract : In the enterprise context, people need to exploit and mainly visualize different types of interactions between heterogeneous objects. Graph model seems to be the most appropriate way to represent those interactions. However, the extracted graphs have in general a huge size which makes it difficult to analyze and visualize. An aggregation step is needed to have more understandable graphs in order to allow users discovering underlying information and hidden relationships between entities. In this work, we propose new measures to evaluate the quality of summaries based on an existing algorithm named k-SNAP that produces a summarized graph according to user-selected node attributes and relationships.
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Chapitre d'ouvrage
Rong Guan and Yves Lechevallier and Gilbert Saporta and Huiwen Wang. Advances in Theory and Applications of High Dimensional and Symbolic Data Analysis, RNTI-E-25, Hermann, pp.157-177, 2013, Revue des Nouvelles Technologies de l'Information, 9782705687335
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https://hal.inria.fr/hal-00838649
Contributeur : Brigitte Trousse <>
Soumis le : mercredi 26 juin 2013 - 10:58:54
Dernière modification le : vendredi 25 mai 2018 - 12:02:04

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  • HAL Id : hal-00838649, version 1

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Amine Louati, Marie-Aude Aufaure, Yves Lechevallier. Graph Aggregation: Application to Social Networks. Rong Guan and Yves Lechevallier and Gilbert Saporta and Huiwen Wang. Advances in Theory and Applications of High Dimensional and Symbolic Data Analysis, RNTI-E-25, Hermann, pp.157-177, 2013, Revue des Nouvelles Technologies de l'Information, 9782705687335. 〈hal-00838649〉

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