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<identifier>oai:HAL:hal-00767050v1</identifier>
<datestamp>2017-12-21</datestamp>
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<title lang=en>Frequent Links: An Approach That Combines Attributes and Structure for Extracting Frequent Patterns in Social Networks</title>
<creator>Stattner, Erick</creator>
<creator>Collard, Martine</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>Advances in Database (Weston, Conn.)s and Information Systems</source>
<source>16th East European Conference, ADBIS 2012, Poznań, Poland, September 18-21, 2012. Proceedings</source>
<source>Advances in Databases and Information Systems (ADBIS)</source>
<coverage>Poznań, Poland</coverage>
<contributor>Springer</contributor>
<identifier>hal-00767050</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00767050</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00767050</source>
<source>Springer. Advances in Databases and Information Systems (ADBIS), 2012, Poznań, Poland. 7503, pp.371-384, 2012, 〈10.1007/978-3-642-33074-2_28〉</source>
<identifier>DOI : 10.1007/978-3-642-33074-2_28</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-33074-2_28</relation>
<language>en</language>
<subject lang=en>Social network</subject>
<subject lang=en>frequent patterns</subject>
<subject lang=en>frequent links</subject>
<subject>[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>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.</description>
<date>2012</date>
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