Visualizing communities in dynamic networks

Abstract : Community structure is relevant to understand the properties of social networks and predict their behavior. But when this study includes the dynamic evolution, finding these communities and following them through time can be even more useful: it may help us to understand how social networks grow and to develop constructive models. In this article we analyze a dynamic blog dataset with a static community detection algorithm based on modularity, and then we use a similarity measure in order to follow the communities through time. Finally we develop a tool to visualize the dynamics of the network. This tool provides a fast intuition about the evolution of the community structure.
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Communication dans un congrès
LAWDN - Latin-American Workshop on Dynamic Networks, Nov 2010, Buenos Aires, Argentina. 4 p., 2010
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https://hal.inria.fr/inria-00531761
Contributeur : José Ignacio Alvarez-Hamelin <>
Soumis le : mercredi 3 novembre 2010 - 16:50:42
Dernière modification le : mercredi 22 août 2018 - 12:00:02
Document(s) archivé(s) le : vendredi 26 octobre 2012 - 14:46:35

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  • HAL Id : inria-00531761, version 1

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Mariano G. Beiro, Jorge Rodolfo Busch, José Ignacio Alvarez-Hamelin. Visualizing communities in dynamic networks. LAWDN - Latin-American Workshop on Dynamic Networks, Nov 2010, Buenos Aires, Argentina. 4 p., 2010. 〈inria-00531761〉

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