Frequent Links: An Approach That Combines Attributes and Structure for Extracting Frequent Patterns in Social Networks

Abstract : In the network modeling area, the most widely used definition of a "pattern" is that of a subgraph, a notion that considers only the network topological structure. While this definition has been very useful for extracting subgraphs frequently found in a network or a set of networks, it does not take into account the node attributes, an intrinsic component of social networks that often provides relevant information on the role or the position of a node in a network. In this paper, we propose a novel approach for extracting frequent patterns in social networks, called frequent link mining, based on the search for particular patterns that combine information on both network structure and node attributes. This kind of patterns, that we call frequent links, provides knowledge on the groups of nodes connected in the social network. In this article, we detail the method proposed for extracting frequent links and discuss its flexibility and its complexity. We demonstrate the efficiency of our solution by carrying out qualitative and quantitative studies.
Type de document :
Communication dans un congrès
Springer. Advances in Databases and Information Systems (ADBIS), 2012, Poznań, Poland. 7503, pp.371-384, 2012, <10.1007/978-3-642-33074-2_28>
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https://hal.archives-ouvertes.fr/hal-00767050
Contributeur : Erick Stattner <>
Soumis le : mercredi 19 décembre 2012 - 13:58:38
Dernière modification le : lundi 21 mars 2016 - 17:27:47

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Erick Stattner, Martine Collard. Frequent Links: An Approach That Combines Attributes and Structure for Extracting Frequent Patterns in Social Networks. Springer. Advances in Databases and Information Systems (ADBIS), 2012, Poznań, Poland. 7503, pp.371-384, 2012, <10.1007/978-3-642-33074-2_28>. <hal-00767050>

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