Descriptive Modeling of Social Networks

Erick Stattner 1 Martine Collard 2
LAMIA - Laboratoire de Mathématiques Informatique et Applications
Abstract : These last years, many analysis methods have been proposed to extract knowledge from social networks. As for the traditional data mining domain, these network-based approaches can be classified according to two main families. The approaches based on predictive modelling, which encompass the techniques that analyse current and historical facts to make predictive assumptions about future or unknown events. The approaches based on descriptive modelling, which cover the set of techniques that aim to summarize the data by identifying some relevant features in order to describe how things organize and actually work. In this paper, we review the main descriptive modelling methods of social networks and show for each of them the resulting useful knowledge on a running example. We particularly emphasize on the most recent methods that combine information available on both the network structure and the node attributes in order to provide original description models taking into account the context.
Type de document :
Article dans une revue
Procedia Computer Science, Elsevier, 2015, 52, pp. 226-233. 〈10.1016/j.procs.2015.05.505〉
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Contributeur : Erick Stattner <>
Soumis le : vendredi 6 mai 2016 - 04:24:51
Dernière modification le : mercredi 18 juillet 2018 - 20:11:28

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Erick Stattner, Martine Collard. Descriptive Modeling of Social Networks. Procedia Computer Science, Elsevier, 2015, 52, pp. 226-233. 〈10.1016/j.procs.2015.05.505〉. 〈hal-01312383〉



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