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Group extraction from professional social network using a new semi-supervised hierarchical clustering

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Abstract

Recently, social network has been given much attention. This paper addresses the issue of extraction groups from professional social network and enriches the representation of the user profile and its related groups through building a social network warehousing. Several criteria may be applied to detect groups within professional communities, such as the area of expertise, the job openings proposed by the group, the security of the group, and the time of the group creation. In this paper, we aim to find, extract, and fuse the LinkedIn users. Indeed, we deal with the group extraction of LinkedIn users based on their profiles using our innovative semi-supervised clustering method based on quantitative constraints ranking. The encouraging experimental results carried out on our real professional warehouse show the usefulness of our approach.

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Notes

  1. The data warehouse is built using the available information at http://www.linkedin.com.

  2. http://www.cs.waikato.ac.nz/~ml/weka/.

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Authors and Affiliations

  1. High Institute of Management of Tunis, University of Tunis, Tunis, Tunisia

    Eya Ben Ahmed

  2. Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia

    Ahlem Nabli

  3. High Institute of Computer and Multimedia of Sfax, University of Sfax, Sfax, Tunisia

    Faïez Gargouri

Authors
  1. Eya Ben Ahmed
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  2. Ahlem Nabli
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  3. Faïez Gargouri
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Correspondence to Eya Ben Ahmed.

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Ben Ahmed, E., Nabli, A. & Gargouri, F. Group extraction from professional social network using a new semi-supervised hierarchical clustering. Knowl Inf Syst 40, 29–47 (2014). https://doi.org/10.1007/s10115-013-0634-x

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  • DOI: https://doi.org/10.1007/s10115-013-0634-x

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