Innovations and Interdisciplinary Solutions for Underserved Areas. Second International Conference, InterSol 2018, Kigali, Rwanda, March 24–25, 2018, Proceedings

Research Article

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

  • @INPROCEEDINGS{10.1007/978-3-319-98878-8_23,
        author={Joseph Ndong and Ibrahima Gueye},
        title={A Robust Process to Identify Pivots Inside Sub-communities in Social Networks},
        proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. Second International Conference, InterSol 2018, Kigali, Rwanda, March 24--25, 2018, Proceedings},
        proceedings_a={INTERSOL},
        year={2018},
        month={9},
        keywords={Social network analysis Community detection Energy Pivot Influencer},
        doi={10.1007/978-3-319-98878-8_23}
    }
    
  • Joseph Ndong
    Ibrahima Gueye
    Year: 2018
    A Robust Process to Identify Pivots Inside Sub-communities in Social Networks
    INTERSOL
    Springer
    DOI: 10.1007/978-3-319-98878-8_23
Joseph Ndong1,*, Ibrahima Gueye2,*
  • 1: University Cheikh Anta Diop
  • 2: Computer Engineering and Telecom, Polytechnic School
*Contact email: joseph.ndong@ucad.edu.sn, igueye@ept.sn

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.