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

Nassira Chekkai, Souham Meshoul, Imene Boukhalfa, Badreddine Chekkai, Amel Ziani, Salim Chikhi

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
Number of pages11
StatePublished - 2019
Externally publishedYes

Publication series

NameLecture Notes in Networks and Systems


  • Adaptation
  • Key players
  • Multi-agent system
  • Social networks
  • Weighted graphs


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