International Conference on Belief Functions

BELIEF 2014: Belief Functions: Theory and Applications pp 115-123 | Cite as

Belief Approach for Social Networks

  • Salma Ben Dhaou
  • Mouloud Kharoune
  • Arnaud Martin
  • Boutheina Ben Yaghlane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8764)


Nowadays, social networks became essential in information exchange between individuals. Indeed, as users of these networks, we can send messages to other people according to the links connecting us. Moreover, given the large volume of exchanged messages, detecting the true nature of the received message becomes a challenge. For this purpose, it is interesting to consider this new tendency with reasoning under uncertainty by using the theory of belief functions. In this paper, we tried to model a social network as being a network of fusion of information and determine the true nature of the received message in a well-defined node by proposing a new model: the belief social network.


Social Networks Belief Network Information Fusion Belief functions 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Janez, F.: Fusion de sources d’information définies sur des référentiels non exhaustifs différents. Ph.D. thesis, Université d’Angers (November 1996)Google Scholar
  2. 2.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD 2003 Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM Press (2003)Google Scholar
  3. 3.
    Khan, A., Bonchi, F., Gionis, A., Gullo, F.: Fast reliability search in uncertain graphs. In: International Conference on Extending Database Technology (EDBT) (2014)Google Scholar
  4. 4.
    Laamari, W., ben Yaghlane, B., Simon, C.: Dynamic directed evidential networks with conditional belief functions:application to system reliability. IPMU (1), 481–490 (2012)Google Scholar
  5. 5.
    Lee, H.: Context reasoning under uncertainty based on evidential fusion networks in home based care. Tech. rep., Faculty of the Graduate School of The University of Texas (2010)Google Scholar
  6. 6.
    Parchas, P., Gullo, F., Papadias, D., Bonchi, F.: The pursuit of a good possibe world: Extracting representative instances of uncertain graphs. In: SIGMOD 2014. ACM Press, Snowbird (2014)Google Scholar
  7. 7.
    Ramasso, E.: Contribution of belief functions to hidden markov models with an application to fault diagnosis. IEEE (1), 1–6 (2009)Google Scholar
  8. 8.
    Shafer, G.: A mathematical theory of evidence. Princeton University Press (1976)Google Scholar
  9. 9.
    Smets, P.: Constructing the pignistic probability function in a context of uncertainty. Uncertainty in Artificial Intelligence 5, 29–39 (1990)CrossRefMATHGoogle Scholar
  10. 10.
    Smets, P.: Belief Functions: the Disjunctive Rule of Combination and the Generalized Bayesian Theorem. International Journal of Approximate Reasoning 9, 1–35 (1993)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Smets, P.: Imperfect information: Imprecision - Uncertainty. In: Motro, A., Smets, P. (eds.) Uncertainty Management in Information Systems, pp. 225–254. Kluwer Academic Publishers (1997)Google Scholar
  12. 12.
    Wasserman, S., Faust, K.: Social Network Analysis, Methods and Application. The Press Syndicate of the University of Cambridge (1994)Google Scholar
  13. 13.
    el Zoghby, N., Cherfaoui, V., Ducourthial, B., Denoeux, T.: Fusion distribuée évidentielle pour la détection d’attaque sybil dans un réseau de véhicules (1), 1–8Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Salma Ben Dhaou
    • 1
    • 2
  • Mouloud Kharoune
    • 1
    • 2
  • Arnaud Martin
    • 1
    • 2
  • Boutheina Ben Yaghlane
    • 1
    • 2
  1. 1.LARODECLe BardoTunisia
  2. 2.IRISA, Université de Rennes 1,IUT de LannionLannion cedexFrance

Personalised recommendations