Elyazid, A. and Brahim, O. and Bouchra, F. (2016) A comparative study of some algorithms for detecting communities in social networks. In: UNSPECIFIED.

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Abstract

In this paper, we studied some methods used in community detection in social networks. In the context of social networks, a community is a set of entities with a lot of interactions among them and little interaction with other sets outside. There are approaches related to static social networks, and others which focus on the dynamic social networks whose structure (actors and links) evolves over time. Static community detection approaches are able to find a division only if a graph is defined for a given time. However, many real graphs have the property to change and evolve over time. That is some nodes and links can appear or disappear during this process of evolution. In order to find communities in such networks we must take into account their different stages of evolution to provide coherent communities not just in a particular point in time, but all along their possible modifications over time. In this paper, we studied the static and dynamic approaches and we made a comparison between different algorithms using several real datasets. © 2016 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Community detection; Comparative studies; Different stages; Dynamic social networks; Nodes and links; Process of evolution; Real data sets; Static and dynamic approach, Population dynamics
Subjects: Computer Science
Divisions: SCIENTIFIC PRODUCTION > Computer Science
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:46
URI: http://eprints.umi.ac.ma/id/eprint/2588

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