MAX-FLMin: An Approach for Mining Maximal Frequent Links and Generating Semantical Structures from Social Networks

Erick Stattner; Martine Collard

doi:10.1007/978-3-642-32600-4_35

MAX-FLMin: An Approach for Mining Maximal Frequent Links and Generating Semantical Structures from Social Networks

Erick Stattner ¹ Martine Collard ¹

1 LAMIA - Laboratoire de Mathématiques Informatique et Applications

Abstract : The paper proposes a new knowledge discovery method called MAX-FLMin for extracting frequent patterns in social networks. Unlike traditional approaches that mainly focus on the network topological structure, the originality of our solution is its ability to exploit information both on the network structure and the attributes of nodes in order to elicit specific regularities that we call "Frequent Links". This kind of patterns provides relevant knowledge about the groups of nodes most connected within the network. First, we detail the method proposed to extract maximal frequent links from social networks. Second, we show how the extracted patterns are used to generate aggregated networks that represent the initial social network with more semantics. Qualitative and quantitative studies are conducted to evaluate the performances of our algorithm in various configurations.

Document type :

Conference papers

Domain :

Computer Science [cs] / Modeling and Simulation

Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00767052
Contributor : Erick Stattner <>
Submitted on : Wednesday, December 19, 2012 - 2:01:52 PM
Last modification on : Wednesday, July 18, 2018 - 8:11:27 PM

MAX-FLMin: An Approach for Mining Maximal Frequent Links and Generating Semantical Structures from Social Networks

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MAX-FLMin: An Approach for Mining Maximal Frequent Links and Generating Semantical Structures from Social Networks

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