WorldCIST'13 -The 2013 World Conference on Information Systems and Technologies

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Social Networks Mining based on Information Retrieval Technologies and Bees Swarm Optimization: Application to DBLP

Yassine Drias

Habiba Drias

Detecting communities is a subject of considerable recent interest in the social and behavioral sciences, biology, economics, marketing and computer science. Lots of studies have been widely undertaken in various directions like community discovery, community mining techniques and community scoring function.
Unlike the previous works where detecting communities is performed on large graphs, our approach considers textual documents for discovering social networks. More precisely, the aim of this paper is to extract social communities from a collection of documents and a query specifying the domain of interest that may link the group. We propose a methodology which develops an information retrieval system capable to generate the documents that are in relationship with any topic. The authors of these documents are linked together to constitute the social community around the given thematic. The search process in the information retrieval system is designed using a bee swarm optimization method in order to optimize the retrieval time. Our approach was implemented and tested on CACM and DBLP and the time of building a social network is quasi instant.


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