Frequent conceptual links and link-based clustering: A comparative analysis of two clustering techniques

Abstract : Numerous social network mining methods have been proposed until now for addressing social mining tasks and specially searching for communities or frequent social patterns. However, the degree of complementarity of these methods has been very little studied. In this paper, we focus on two knowledge extraction processes in social networks: a link-based clustering that may extract social communities and a recent approach that searches for frequent conceptual link involving both clustering and search for frequent social patterns. We explore how the models extracted by each method may match and which potential useful knowledge they may provide. Our objective is to evaluate the potential relationships between communities and frequent conceptual links. For this purpose, we propose a set of measures for evaluating the degree to which these patterns extracted from the same dataset are matching. Our approach is applied on two datasets and demonstrates the importance of considering simultaneously various kinds of knowledge and their complementarity.
Type de document :
Communication dans un congrès
IEEE/ACM. 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), 2013, Niagara falls, Canada. pp.1-8, 2013
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https://hal.archives-ouvertes.fr/hal-00874351
Contributeur : Erick Stattner <>
Soumis le : jeudi 17 octobre 2013 - 15:57:21
Dernière modification le : lundi 21 mars 2016 - 17:37:28

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  • HAL Id : hal-00874351, version 1

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Erick Stattner, Martine Collard. Frequent conceptual links and link-based clustering: A comparative analysis of two clustering techniques. IEEE/ACM. 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), 2013, Niagara falls, Canada. pp.1-8, 2013. <hal-00874351>

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