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

Related Works

Social networks mining has been widely studied this last decade and several works have reached important advancements in the area. Applications to domains like biology, economics and marketing have been also undertaken and the results were promising. Among the main issues that interest the researchers are the mining methods, community identification and modeling social rating networks. Lots of investments such those of (Clauset et al. 2004, Flake et al. 2000, Fortunato 2010, Newman & Girvan 2004, Radicchi et al. 2004) have been devoted to community identification. All of these articles build the social network from a given large graph and differ from each other by the method designed to extract the community structure. In (Flake et al. 2000), the authors focused especially on web communities and in (Domingos & Richardson 2001), the authors considered the marketing application. Other axes that were also investigated concern the analysis of the social network as in (Leskovec et al. 2008) and the scoring and evaluation of the social community as in (Newman & Girvan 2004, Domingos & Richardson 2001). The community scoring function quantifies how ‘efficient’ is the community.

On the other hand, information retrieval has known extremely interesting developments for more than four decades. The general concepts and techniques are well described in (Christopher et al. 2008, Rijsbergen 1979, Salton 1976).

Finally BSO, the bee swarm optimization approach is introduced in (Drias et al. 2005) and one of its applications to web information retrieval is published in (Drias et al. 2010).