Identifying Knots of Trust in Virtual Communities

  • Nurit Gal-Oz
  • Ran Yahalom
  • Ehud Gudes
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 358)


Knots of trust are groups of community members having overall “strong” trust relations between them. In previous work we introduced the knot aware trust based reputation model. According to this model, in order to provide a member with reputation information relative to her viewpoint, the system must identify the knot to which that member belongs and interpret its reputation data correctly. In the current paper we present the problem of identifying knots which is modeled as a graph clustering problem, where vertices correspond to individuals and edges describe trust relationships between them. We propose a new perspective for clustering that reflects the subjective idea of trust and the nature of the community. A class of weight functions is suggested for assigning edge weights and their impact on the stability and strength of knots is demonstrated. Finally we show the efficiency of knots of high quality for providing their members with relevant reputation information.


Virtual Community Graph Cluster Community Graph Trust Relation Reputation Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Ailon, N., Charikar, M., Newman, A.: Aggregating inconsistent information: ranking and clustering. Journal of the ACM (JACM) 55(5), 1–27 (2008)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Bansal, N., Blum, A., Chawla, S.: Correlation clustering. In: Proceedings of the 43rd Symposium on Foundations of Computer Science (FOCS 2002), pp. 238–247. IEEE Computer Society, Washington, DC, USA (2002)CrossRefGoogle Scholar
  6. 6.
    Chakraborty, S., Ray, I.: Trustbac: integrating trust relationships into the rbac model for access control in open systems. In: Proceedings of the 11th ACM Symposium on Access Control Models and Technologies (SACMAT 2006), pp. 49–58. ACM, New York (2006)CrossRefGoogle Scholar
  7. 7.
    Charikar, M., Guruswami, V., Wirth, A.: Clustering with qualitative information. Journal of Computer and System Sciences 71(3), 360–383 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Cohen, W.W., Richman, J.: Learning to match and cluster large high-dimensional data sets for data integration. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 475–480 (2002)Google Scholar
  9. 9.
    Demaine, E.D., Emanuel, D., Fiat, A., Immorlica, N.: Correlation clustering in general weighted graphs. Theoretical Computer Science 361(2-3), 172–187 (2006), Special issue on approximation and online algorithmszbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Edachery, J., Sen, A., Brandenburg, F.J.: Graph clustering using distance-k cliques. Graph Drawing, 98–106 (1999)Google Scholar
  11. 11.
    Elsner, M., Schudy, W.: Bounding and comparing methods for correlation clustering beyond ilp. In: NAACL-HLT Workshop on Integer Linear Programming for Natural Language Processing (ILPNLP 2009), pp. 19–27 (2009)Google Scholar
  12. 12.
    Gal-Oz, N., Gudes, E., Hendler, D.: A robust and knot-aware trust-based reputation model. In: Proceedings of the 2nd Joint iTrust and PST Conferences on Privacy, Trust Management and Security (IFIPTM 2008), Trondheim, Norway, pp. 167–182 (June 2008)Google Scholar
  13. 13.
    Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD) 1(1), 4 (2007)CrossRefGoogle Scholar
  14. 14.
    Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web (WWW 2004), pp. 403–412. ACM, New York (2004)CrossRefGoogle Scholar
  15. 15.
    Jøsang, A., Gray, E., Kinateder, M.: Analysing topologies of transitive trust. In: Proceedings of the 1st International Workshop on Formal Aspects in Security and Trust (FAST 2003), pp. 9–22 (2003)Google Scholar
  16. 16.
    Kamvar, S.D., Schlosser, M.T., Garcia-Molina, H.: The eigentrust algorithm for reputation management in p2p networks. In: Proceedings of the 12th International Conference on World Wide Web (WWW 2003), pp. 640–651. ACM, New York (2003)Google Scholar
  17. 17.
    Kinateder, M., Baschny, E., Rothermel, K.: Towards a generic trust model – comparison of various trust update algorithms. In: Herrmann, P., Issarny, V., Shiu, S.C.K. (eds.) iTrust 2005. LNCS, vol. 3477, pp. 177–192. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Massa, P., Avesani, P.: Avesani. Controversial users demand local trust metrics: An experimental study on epinions. com community. In: Proceedings of the National Conference on Artificial Intelligence (AAAI 2005), vol. 20, p. 121 (2005)Google Scholar
  19. 19.
    Matthews, B.W.: Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta (BBA)-Protein Structure 405(2), 442–451 (1975)CrossRefGoogle Scholar
  20. 20.
    Meilă, M.: Comparing clusterings–an information based distance. Journal of Multivariate Analysis 98(5), 873–895 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Ng, V., Gardent, C.: Improving machine learning approaches to coreference resolution. In: ACL, pp. 104–111 (2002)Google Scholar
  22. 22.
    Soon, W.M., Ng, H.T., Lim, D.C.Y.: A machine learning approach to coreference resolution of noun phrases. Computational Linguistics 27(4), 521–544 (2001)CrossRefGoogle Scholar
  23. 23.
    Swamy, C.: Correlation clustering: maximizing agreements via semidefinite programming. In: Proceedings of the Fifteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 526–527. Society for Industrial and Applied Mathematics, Philadelphia (2004)Google Scholar
  24. 24.
    Ward, J.H., Hook, M.E.: Application of an Hierarchical Grouping Procedure to a Problem of Grouping Profiles, vol. 23(1), pp. 69–82 (1963)Google Scholar

Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • Nurit Gal-Oz
    • 1
  • Ran Yahalom
    • 1
  • Ehud Gudes
    • 1
  1. 1.Deutsche Telekom LaboratoriesBen-Gurion UniversityBeer-ShevaIsrael

Personalised recommendations