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Title: Bees colonies for detecting communities evolution using data warehouse

Authors: Yasmine Chaabani; Jalel Akaichi

Addresses: Department of Computer Science, Higher Institute of Management, University of Tunis, Tunisia ' Department of Computer Science, Higher Institute of Management, University of Tunis, Tunisia; Department of Computer Science University of Bisha, Saudi Arabia

Abstract: The analysis of social networks and their evolution has gained much interest in recent years. In fact, few methods revealed and tracked meaningful communities over time. These methods also dealt efficiently with structure and topic evolution of networks. In this paper, we propose a novel technique to track dynamic communities and their evolution behaviour. The main objective of our approach and using the artificial bee colony (ABC) is to trace the evolution of community and to optimise our objective function to keep proper partitioning. Moreover, we use a data warehouse as a mind of bees to store the information of different communities structure in every timestamp. The experimental results showed that the proposed method is efficient in discovering dynamics communities and tracking their evolution.

Keywords: social network; community detection; bees colony.

DOI: 10.1504/IJDMMM.2020.106720

International Journal of Data Mining, Modelling and Management, 2020 Vol.12 No.2, pp.192 - 206

Received: 22 Nov 2017
Accepted: 14 Nov 2018

Published online: 31 Mar 2020 *

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