AMAM: Adaptive Multi-Agents Based Model for Negative Key Players Identification in Social Networks

  • Nassira ChekkaiEmail author
  • Souham Meshoul
  • Imene Boukhalfa
  • Badreddine Chekkai
  • Amel Ziani
  • Salim Chikhi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 50)


Social Network Analysis (SNA) is an active research topic. It arises in a broad range of fields. One important issue in SNA is the discovery of key players who are the most influential actors in a social network. Negative Key Player Problem (KPP-NEG) aims at finding the set of actors whose removal will break the social network into fragments. By another way, Multi-Agents Systems (MAS) paradigm suggests suitable ways to design adaptive systems that exhibit desirable properties such as reaction, learning, reasoning and evolution. A fortiori, the intrinsic nature of social networks and the requirements of their analysis could be efficiently handled using a MAS framework. Within this context, this paper proposes a multi-agents based-model AMAM for KPP-NEG. We first represent the social network in terms of a weighted graph. Then, a set of agents cooperate in order to identify the most important nodes. Simulation and computational results are demonstrated to confirm the effectiveness of our approach.


Key players KPP-NEG Social networks Multi-agent system Adaptation Weighted graphs 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nassira Chekkai
    • 1
    Email author
  • Souham Meshoul
    • 1
  • Imene Boukhalfa
    • 1
  • Badreddine Chekkai
    • 1
  • Amel Ziani
    • 2
  • Salim Chikhi
    • 1
  1. 1.Abdelhamid Mehri-Constantine 2 UniversityConstantineAlgeria
  2. 2.University of Badji MokhtarAnnabaAlgeria

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