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A Robust Process to Identify Pivots Inside Sub-communities in Social Networks

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Innovations and Interdisciplinary Solutions for Underserved Areas (InterSol 2018)

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Abstract

In this work, we extend a previous work where we proposed a suitable state model built from a Karhunen-Loeve Transformation to build a new decision process from which, we can extract useful knowledge and information about the identified underlying sub-communities from an initial network. The aim of the method is to build a framework for a multi-level knowledge retrieval. Besides the capacity of the methodology to reduce the high dimensionality of the data, the new detection scheme is able to extract, from the sub-communities, the dense sub-groups with the definition and formulation of new quantities related to the notions of energy and co-energy. The energy of a node is defined as the rate of its participation to the set of activities while the notion of co-energy defines the rate of interaction/link between two nodes. These two important features are used to make each link weighted and bounded, so that we are able to perform a thorough refinement of the sub-community discovery. This study allows to perform a multi-level analysis by extracting information either per-link or per-intra-sub-community. As an improvement of this work, we define the notion of pivot to relate the node(s) with the greatest influence in the network. We propose the use of a thorough tool based on the formulation of the transformation of a suitable probabilistic model into a possibilistic model to extract these pivot(s) which are the nodes that control the evolution of the community.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University Cheikh Anta Diop, BP 5005, Fann Dakar, Senegal

    Joseph Ndong

  2. Computer Engineering and Telecom, Polytechnic School, Thies, Senegal

    Ibrahima Gueye

Authors
  1. Joseph Ndong
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  2. Ibrahima Gueye
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Corresponding author

Correspondence to Ibrahima Gueye .

Editor information

Editors and Affiliations

  1. Cheikh Anta Diop University, Dakar, Senegal

    Cheikh M.F. Kebe

  2. University System of Maryland, Bambey, Senegal

    Assane Gueye

  3. Assane University, Seck de Ziguinchor, Senegal

    Ababacar Ndiaye

  4. Carnegie Mellon University, Kigali, Rwanda

    Aminata Garba

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Ndong, J., Gueye, I. (2018). A Robust Process to Identify Pivots Inside Sub-communities in Social Networks. In: Kebe, C., Gueye, A., Ndiaye, A., Garba, A. (eds) Innovations and Interdisciplinary Solutions for Underserved Areas. InterSol 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 249. Springer, Cham. https://doi.org/10.1007/978-3-319-98878-8_23

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  • DOI: https://doi.org/10.1007/978-3-319-98878-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98877-1

  • Online ISBN: 978-3-319-98878-8

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