Social Networks Discovery Based on Information Retrieval Technologies and Bees Swarm Optimization: Application to DBLP

Yassine Drias and Habiba Drias
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Yassine Drias: Department of Computer Science, USTHB, LRIA, Algiers, Algeria
Habiba Drias: Department of Computer Science, USTHB, LRIA, Algiers, Algeria

International Journal of Systems and Service-Oriented Engineering (IJSSOE), 2014, vol. 4, issue 3, 46-65

Abstract: Unlike the previous works where detecting communities is performed on large graphs, our approach considers textual documents for discovering potential 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 that 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 BSO, the bee swarm optimization method in order to optimize the retrieval time for large amount of documents. Our approach was implemented and tested on CACM and DBLP and the time of building a social network is quasi instant.

Date: 2014
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