Uncertainty-Preserving Trust Prediction in Social Networks

  • Anna Stachowiak
Part of the Studies in Computational Intelligence book series (SCI, volume 526)


The trust metric has became an increasingly important element of social networks. While collecting, processing and sharing information is becoming easier and easier, the problem of the quality and reliability of that information remains a significant one. The existence of a trust network and methods of predicting trust between users who do not know each other are intended to help in forming opinions about how much to trust information from distant sources. In this chapter we discuss some basic concepts like trust modeling, trust propagation and trust aggregation. We briefly recall recent developments in the area and present a new approach that focuses on the uncertainty aspect of trust value. To this end we utilize a theory of incompletely known fuzzy sets and we introduce a new, uncertainty-preserving trust prediction operator based on group opinion and on relative scalar cardinality of incompletely known fuzzy sets. Motivated by the need for proper uncertainty processing, we have constructed a new method of calculating relative scalar cardinality of incompletely known fuzzy sets that ensures the monotonicity of uncertainty. We outline the problem of uncertainty propagation, and we illustrate by examples that the proposed operator provides most of the desirable properties of trust and uncertainty propagation and aggregation.


Trust propagation Trust aggregation Uncertainty Incompletely known fuzzy sets Relative cardinality 


  1. 1.
    Artz, D., Gil, Y.: A survey of trust in computer science and the semantic web. J. Web Semant. 5, 58–71 (2007)CrossRefGoogle Scholar
  2. 2.
    Mui, L., Mohtashemi, M., Halberstadt, A.: A computational model of trust and reputation. In: Proceedings of the 35th International Conference on System Science, pp. 280–287 (2002)Google Scholar
  3. 3.
    Kamvar, S., Schlosser, M., Garcia-Molin, H.: The eigentrust algorithm for reputation management in p2p networks. In: Proceedings of the 12th International World Wide Web Conference, pp. 640–651 (2003)Google Scholar
  4. 4.
    Cornelli, F., Damiani, E., Capitani, S.D., Paraboschi, S., Samarati, P.: Choosing reputable servents in a p2p network. In: Proceedings of the 11th World Wide Web Conference, pp. 376–386 (2002)Google Scholar
  5. 5.
    Zaihrayeu, I., Silva, P.P.D., Mcguinness, D.L., Zaihrayeu, I., Pinheiro, P., Deborah, S., Mcguinness, L.: Iwtrust: Improving user trust in answers from the web. In: Proceedings of 3rd International Conference on Trust Management, pp. 384–392. Springer (2005)Google Scholar
  6. 6.
    Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. Lect. Notes Comput. Sci. 3290, 492–508 (2004)CrossRefGoogle Scholar
  7. 7.
    Golbeck, J.: Generating predictive movie recommendations from trust in social networks. In: Proceedings of the Fourth International Conference on Trust Management, Springer (2006)Google Scholar
  8. 8.
    Victor, P., Cornelis, C., Cock, M.D., da Silva, P.P.: Gradual trust and distrust in recommender systems. Fuzzy Sets Syst. 160(10), 1367–1382 (2009)CrossRefMATHGoogle Scholar
  9. 9.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: IUI, pp. 167–174 (2005) Google Scholar
  10. 10.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, Berlin (2011)Google Scholar
  11. 11.
    Sinha, R., Sinha, R., Swearingen, K.: Comparing recommendations made by online systems and friends. In: Proceedings of the DELOS-NSF Workshop on Personalization and Recommender Systems in Digital Libraries (2001)Google Scholar
  12. 12.
    Dyczkowski, K., Stachowiak, A.: A recommender system with uncertainty on the example of political elections. In: Advances in Computational Intelligence; Communications in Computer and Information Science, vol. 298, pp. 441–449. Springer, Berlin (2012)Google Scholar
  13. 13.
    Josang, A., Knapskog, S.J.: A metric for trusted systems. In: Proceedings of NIST-NCSC1998, pp. 16–29 (1998)Google Scholar
  14. 14.
    Abdul-Rahman, A., Hailes, S.: Supporting trust in virtual communities. In: Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS’00), vol. 6, pp. 1769–1777. IEEE Computer Society, Orlando (2000)Google Scholar
  15. 15.
    Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the World Wide Web Conference, pp. 403–412. ACM Press, New York (2004)Google Scholar
  16. 16.
    Cock, M.D., da Silva, P.P.: A many valued representation and propagation of trust and distrust. In: WILF. Lecture Notes in Computer Science, vol. 3849, pp. 114–120. Springer, Berlin (2005)Google Scholar
  17. 17.
    Arieli, O., Deschrijver, G., Kerre, E.: Uncertainty modeling by bilattice-based squares and triangles. IEEE Trans. Fuzzy Syst. 15, 161–175 (2007)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Victor, P., Cornelis, C., De Cock, M., Herrera-Viedma, E.: Bilattice-based aggregation operators for gradual trust and distrust. In: World Scientific Proceedings Series on Computer Engineering and Information Science, pp. 505–510. World Scientific, Singapore (2010)Google Scholar
  19. 19.
    Levien, R., Aiken, A.: Attack resistant trust metrics for public key certification. In: 7th USENIX Security Symposium, pp. 229–242. (1998)Google Scholar
  20. 20.
    Golbeck, J.A.: Computing and applying trust in web-based social networks (2005)Google Scholar
  21. 21.
    Ziegler, C.N., Lausen, G.: Spreading activation models for trust propagation. In: Proceedings of the IEEE International Conference on e-Technology, e-Commerce, and e-Service, IEEE Computer Society Press, Taipei (2004)Google Scholar
  22. 22.
    Kuter, U., Golbeck, J.: Using probabilistic confidence models for trust inference in web-based social networks. ACM Trans. Internet Technol. 10(2), 1–23 (2006)Google Scholar
  23. 23.
    Massa, P., Avesani, P.: Trust metrics on controversial users: balancing between tyranny of the majority and echo chambers. Int. J. Semant. Web Inf. Syst. 3, 39–64 (2007)CrossRefGoogle Scholar
  24. 24.
    Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Ginsberg, M.L.: Multi-valued logics: a uniform approach to reasoning in artificial intelligence. Comput. Intell. 4, 256–316 (1988)Google Scholar
  26. 26.
    Zadeh, L.: The concept of a linguistic variable and its applications to approximate reasoning i. Inf. Sci. 8, 199–249 (1975)MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Atanassov, K., Stoeva, S.: Intuitionistic fuzzy sets. In: Proceedings Polish Symposium on Interval and Fuzzy Mathematics, pp. 23–26. Poznan (1983)Google Scholar
  28. 28.
    Atanassov, K.: Intuitionistic Fuzzy Sets: theory and Applications. Physica-Verlag, Heidelberg (1999)CrossRefMATHGoogle Scholar
  29. 29.
    Atanassov, K., Gargov, G.: Interval valued intuitionistic fuzzy sets. Fuzzy Sets Syst. 31, 343–349 (1989)MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    Cornelis, C., Atanassov, K., Kerre, E.: Intuitionistic fuzzy sets and interval-valued fuzzy sets: a critical comparison. In: Proceedings of Third European Conference on Fuzzy Logic and Technology (EUSFLAT’03), pp. 159–163. Zittau (2003)Google Scholar
  31. 31.
    Deschrijver, G., Kerre, E.: On the relationship between some extensions of fuzzy set theory. Fuzzy Sets Syst. 133, 227–235 (2003)MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Luca, A.D., Termini, S.: A definition of non probabilistic entropy in the setting of fuzzy sets theory. Inf. Comput. 20, 301–312 (1972)MATHGoogle Scholar
  33. 33.
    Szmidt, E., Kacprzyk, J.: Entropy for intuitionistic fuzzy sets. Fuzzy Sets Syst. 118, 467–477 (2001)MathSciNetCrossRefMATHGoogle Scholar
  34. 34.
    Pankowska, A., Wygralak, M.: General if-sets with triangular norms and their applications to group decision making. Inf. Sci. 176, 2713–2754 (2006)MathSciNetCrossRefMATHGoogle Scholar
  35. 35.
    Danan, E., Ziegelmeyer, A.: Are preferences incomplete? an experimental study using flexible choices. Papers on strategic interaction, Max Planck Institute of Economics, Strategic Interaction Group (2004)Google Scholar
  36. 36.
    Mendel, J.M., John, R.I.: Type-2 fuzzy sets made simple, IEEE Trans. Fuzzy Syst. 10, 117–127 (2002)Google Scholar
  37. 37.
    Cornelis, C., Kerre, E.: Inclusion measures in intuitionistic fuzzy set theory. In: Nielsen, T., Zhang, N. (eds.) Symbolic and Quantitative Approaches to Reasoning with Uncertainty, vol. 2711, pp. 345–356. Lecture Notes in Computer ScienceSpringer, Berlin (2003)CrossRefGoogle Scholar
  38. 38.
    Niewiadomski, A., Ochelska, J., Szczepaniak, P.S.: Interval-valued linguistic summaries of databases. Control Cybern. 35, 415–443 (2006)MATHGoogle Scholar
  39. 39.
    Stachowiak, A.: Propagating and aggregating trust with uncertainty measure. In Jedrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) ICCCI (1). Lecture Notes in Computer Science, vol. 6922, pp. 285–293. Springer (2011)Google Scholar
  40. 40.
    Grzegorzewski, P.: Conditional probability and independence of intuitionistic fuzzy events. Notes Intuitionistic Fuzzy Sets 6, 7–14 (2000)MathSciNetMATHGoogle Scholar
  41. 41.
    Grzegorzewski, P.: On possible and necessary inclusion of intuitionistic fuzzy sets. Inf. Sci. 181, 342–350 (2011)MathSciNetCrossRefMATHGoogle Scholar
  42. 42.
    Zadeh, L.: A computational approach to fuzzy quantifiers in natural languages. Comput. Math. Appl. 9, 149–184 (1983)MathSciNetCrossRefMATHGoogle Scholar
  43. 43.
    Yager, R.R.: Interpreting linguistically quantified propositions. Int. J. Intell. Syst. 9, 541–569 (1994)CrossRefMATHGoogle Scholar
  44. 44.
    Kacprzyk, J.: Group decision making with a fuzzy linguistic majority. Fuzzy Sets Syst. 18, 105–118 (1986)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Mathematics and Computer ScienceAdam Mickiewicz UniversityPoznańPoland

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