Advertisement

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
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 50)

Abstract

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.

Keywords

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

References

  1. 1.
    Hoppe, B., Reinelt, C.: Social network analysis and the evaluation of leadership networks. Leadersh. Q. 2(4), 600–619 (2010)CrossRefGoogle Scholar
  2. 2.
    Arroyo, D.O., Akbar Hussain, D.M.: An information theory approach to identify sets of key players. In: Proceedings of the 1st European Conference on Intelligence and Security Informatics (EuroISI 2008), pp. 15–26 (2008)Google Scholar
  3. 3.
    Lin, P., Chen, L., Yuan, M., Nie, P.: Discover the misinformation broadcasting in on-line social networks. J. Inf. Sci. Eng. 31(3), 763–785 (2015)Google Scholar
  4. 4.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2001), pp. 57–66, New York, NY, USA (2001)Google Scholar
  5. 5.
    Watts, D., Dodds, P.: Influentials, networks, and public opinion formation. J. Consum. Res. 34(4), 441–458 (2007)CrossRefGoogle Scholar
  6. 6.
    Ouimet, G.: Pour une psychologie du changement L’incontournable décodage de la culture. Direction de la recherche, Editor (HEC Montréal), Canada (2005)Google Scholar
  7. 7.
    Borgatti, P.: Identifying sets of key players in a network. Comput. Math. Organ. Theor. 12(1), 21–34 (2006)CrossRefGoogle Scholar
  8. 8.
    Lindquist, M.J., Zenou, Y.: Key players in co-offending networks. IZA, Margard Ody, P.O. Box 7240, D-53072 Bonn, Germany (2014)Google Scholar
  9. 9.
    Arulselvan, A., Commander, C.W., Pardalos, P.M., Shylo, O.: Managing network risk via critical node identification. In: Gulpinar, N., Rustem, B. (eds.) Risk Management in Telecommunication Networks 2009. Springer, Heidelberg (2009)Google Scholar
  10. 10.
    Qi, X., Fuller, E., Wu, Q., Wu, Y., Zhang, C.Q.: Laplacian centrality: a new centrality measure for weighted networks. Inf. Sci. 194, 240–253 (2012)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Opsahl, T., Agneessensb, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)CrossRefGoogle Scholar
  12. 12.
    Avrachenkov, K.E., Mazalov, V.V., Tsynguev, B.T.: Beta current flow centrality for weighted networks. In: Proceedings of the 4th International Conference (CSoNet 2015), Beijing, China (2015)Google Scholar
  13. 13.
    Justification and Application of Eigenvector Centrality. https://www.math.washington.edu/~morrow/336_11/papers/leo.pdf. Accessed 24 Jul 2018
  14. 14.
    Latora, V., Marchiori, M.: How the science of complex networks can help developing strategies against terrorism. Chaos Solitons Fractals 20(1), 69–75 (2004)CrossRefGoogle Scholar
  15. 15.
    Eugene, K.Y., Alex, N.C.L., Alvin, W.S.: Characterizing terrorist networks using efficiency method. USC3001 Term Paper, 2005–2006Google Scholar
  16. 16.
    Arroyo, D.O.: Discovering sets of key players in social networks. In: Computational Social Network Analysis, pp. 27–47. Springer, Heidelberg (2010)Google Scholar
  17. 17.
    Tarjan, R.: Depth first search and linear graph algorithms. SIAM J. Comput. 1(2), 146–160 (1972)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Everton, S.F.: Network topography, key players and terrorist networks. Connect. J. 32(1), 12–19 (2012)Google Scholar
  19. 19.
    Java Agent Development Framework «JADE». http://jade.tilab.com/. Accessed 22 Feb 2016
  20. 20.
    Victor, P., Cornelis, C., De Cock, M., Teredesai, A.M.: Key figure impact in trust-enhanced recommender systems. AI Commun. 21, 127–143 (2008)MathSciNetMATHGoogle Scholar
  21. 21.
    OpinRank Dataset. http://kavita-ganesan.com/entity-ranking-data/#.WrKVgOzwbIV. Accessed 21 Mar 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nassira Chekkai
    • 1
  • 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

Personalised recommendations