Title: A new model for communities' detection in dynamic social networks inspired from human families
Authors: Rachid Djerbi; Mourad Amad; Rabah Imache
Addresses: LIMOSE Laboratory, Computer Science Department, Faculty of Sciences, University M'Hamed Bougara, Boumerdes, Independency Avenue, 35000, Algeria ' LIMPAF Laboratory, Computer Science Department, Bouira University, 10000 Bouira, Algeria ' LIMOSE Laboratory, Computer Science Department, Faculty of Sciences, University M'Hamed Bougara, Boumerdes, Independency Avenue, 35000, Algeria
Abstract: Nowadays, social networks have been widely used by different people for different purposes in the world. The discovering of communities is a widespread subject in the space of social networks analysis. Many interesting solutions have been proposed in the literature. However, most solutions have common problems: the stability and the community structures quality. In this paper, we propose a new model to find communities based on a new concept called 'large families'. This model will be used, to motivate a community detection strategy to identify and effectively monitor the evolution of dynamic communities. We propose a compromise between the stability and the quality metrics. We apply our model on a real social network of the karate club of Zachary. Also, we describe experiences of our model on a large scale network of Enron's email data set as broader Benchmark Network. Simulations results show that our proposed model is globally satisfactory.
Keywords: dynamic social networks; community detection; communities overlap; large families; quality of community structures; stability.
International Journal of Internet Technology and Secured Transactions, 2020 Vol.10 No.1/2, pp.24 - 60
Received: 19 Feb 2018
Accepted: 04 May 2018
Published online: 20 Jan 2020 *