Bayesian Networks Based Trust Model in Social Networks

Document Type : Original Article

Authors

-

Abstract

Social networks, networks that have come into existence, are on the Internet, whose purpose of the establishment is to communicate with different people from different societies. Social networks are a developed form whose information is not trusted by all individuals. Although, it is a popular network that can provide trusted information for some people. If one or more users receive some information from oth-ers, they should assure they have not recieved incorrect data from malicious users. Solutions to these prob-lems are confidence models. Provided that trust deals withpossibilities, Bayesian networks use possibilities to solve problems. As a result, the Bayesian network can improve the calculation of trust. In this study, the proposed model (BTSN) presents a model for calculating confidence using Bayesian networks for social networking. This model is able to calculate the confidence accurately and, in a large scale, can be used in social networks. In addition, the the performance and methods have been studied .

References

   [1]      J. Caverlee, L. Liu, and S. Webb, “Socialtrust:  tamper-resilient trust establishment in online communities,” in Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries, pp. 104–114, 2008.
   [2]      A. Jøsang and S. Pope, “Semantic constraints for trust transitivity,” in Proceedings of the 2nd Asia-Pacific conference on Conceptual modelling, vol. 43, pp. 59–68, 2005.
   [3]      P. Sztompka, “Trust: A sociological theory,” Cambridge University Press, 1999.
   [4]      J. A. Golbeck, “Computing and applying trust in web-based social networks,” 2005.
   [5]      S. P. Marsh, “Formalising trust as a computational concept,” 1994.
   [6]      L. Mui, M. Mohtashemi, and A. Halberstadt, “A computational model of trust and reputation,” in System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on, pp. 2431–2439, 2002.
   [7]      J. Golbeck, “Trust and nuanced profile similarity in online social networks,” ACM Trans. Web, vol. 3, no. 4, p. 12, 2009.
   [8]      M. Maheswaran, H. C. Tang, and A. Ghunaim, “Towards a gravity-based trust model for social networking systems,” in Distributed Computing Systems Workshops, 2007. ICDCSW’07. 27th International Conference on, p. 24, 2007.
   [9]      M. Li and A. Bonti, “T-OSN: a trust evaluation model in online social networks,” in Embedded and Ubiquitous Computing (EUC), 2011 IFIP 9th International Conference on, pp. 469–473, 2011.
[10]      R. Levien, “Attack-resistant trust metrics,” in Computing with Social Trust, Springer, pp. 121–132, 2009.
[11]      S. Al-Oufi, H.-N. Kim, and A. El Saddik, “A group trust metric for identifying people of trust in online social networks,” Expert Syst. Appl., vol. 39, no. 18, pp.     13173–13181, Dec. 2012.
[12]      P. Massa and P. Avesani, “Trust metrics on controversial users: Balancing between tyranny of the majority,” Int. J. Semant. Web Inf. Syst., vol. 3, no. 1, pp. 39–64, 2007.
[13]      J. Caverlee, L. Liu, and S. Webb, “Towards robust trust establishment in web-based social networks with socialtrust,” in Proceedings of the 17th international conference on World Wide Web, pp. 1163–1164, 2008.
[14]      U. Kuter and J. Golbeck, “Sunny: A new algorithm for trust inference in social networks using probabilistic confidence models,” in AAAI, vol. 7, pp. 1377–1382, 2007.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
[15]      S. Fong, Y. Zhuang, M. Yu, and I. Ma, “Quantitative analysis of trust factors on social network using data mining approach,” in Future Generation Communication Technology (FGCT), 2012 International Conference on, pp. 70–75, 2012.
[16]      L. Bouchaala, A. Masmoudi, F. Gargouri, and A. Rebai, “Improving algorithms for structure learning in Bayesian Networks using a new implicit score,” Expert Syst. Appl., vol. 37, no. 7, pp. 5470–5475, 2010.
[17]      G. F. Cooper and E. Herskovits, “A Bayesian method for the induction of probabilistic networks from data,” Mach. Learn., vol. 9, no. 4, pp. 309–347, 1992.
[18]      I. Ebert-Uphoff, “A Probability-Based Approach to Soft Discretization for Bayesian Networks,” 2009.
[19]      Y. Wang and J. Vassileva, “Bayesian Network-Based Trust Model,” University of Saskatchewan, Computer Science Department,Saskatoon, Saskatchewan, S7N 5A9, Canada, 2003.

Files

History

  • Receive Date: 09 April 2017
  • Revise Date: 20 February 2019
  • Accept Date: 19 September 2018
  • Publish Date: 23 July 2018

Share

How to cite

Statistics