Trust Inference in Social Networks by Combination of Neural Network and Genetic Algorithm

Editorial

Authors

1 Faculty of Electrical and Computer Engineering, Islamic Azad University, Birjand, Iran

2 Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran

3 - Faculty of Engineering, Qom University, Qom, Iran

Abstract

The trust inference problem in a social network is defined as anticipating the trust level that a user can have to another user who is not directly connected to him on the trust network. This research aims to propose a method for trust inference in a trust network. Previous research studies are mainly limited to one type of trust network, and they cannot be used for different trust networks with different values of trust. In this research, soft computing and neural network model are used to predict trust values. To train the neural network system, genetic algorithm is exploited. One of the main advantages of the proposed method is that, unlike previous methods, it is not limited to one type of trust network, and it can also be used for trust networks with different values of trust. In the proposed method, at first four proposed features are extracted from the trust network, and afterward, the proposed neural network system is trained using these features as well as the genetic algorithm. The proposed method is implemented on the standard trust network and is compared with other similar methods. Experimental results indicate that the proposed method is able to produce more accurate results in comparison with previous methods.

Keywords

References

[1]      Y. Wang, Z. Cai, G. Yin, Y. Gao, and Q. Pan, "A trust measurement in social networks based on game theory," in International Conference on Computational Social Networks, 2015, pp. 236-247: Springer.
[2]      J. Golbeck, "Trust on the world wide web: A survey," Found. Trends Web Sci. , vol. 1, no. 2, pp. 131–197, 2006.
[3]      J. A. Golbeck, Computing And A Applying Trust In Web-Based Social Networks, PhD thesis, Department of Computer Science, University of Maryland, Maryland, College Park, MD, USA, 2005.
[4]      S. Hamdi, A. L. Gancarski, A. Bouzeghoub, and S. B. Yahia, "Tison: Trust inference in trust-oriented social networks," ACM Transactions on Information Systems (TOIS), vol. 34, no. 3, p. 17, 2016.
[5]      S. Chen, G. Wang, and W. Jia, "κ-FuzzyTrust: efficient trust computation for large-scale mobile social networks using a fuzzy implicit social graph," Information Sciences, vol. 318, pp. 123-143, 2015.
[6]      (2017). Trust Project Network. Available: http://trust.mindswap.org.
[7]      (1/15/2018) .FilmTrust. Available: https://www.librec.net/datasets/filmtrust.zip.
[8]      J. Scott, Social Network Analysis. Sage, 2017.
[9]      E. Elsalamouny, V. Sassone, and M. Nielsen, "HMM-based trust model," in 6th International Workshop on Formal Aspects on Security and Trust (FAST),  vol. 5983, pp. 21–35 ,2010.
[10]      R. Xiang, J. Neville, and M. Rogati, "Modeling relationship strength in online social networks," in 19th International Conference on World Wide Web (WWW’10), New York, 2010, pp. 981–990: ACM Press.
[11]      A. Josang, "Probabilistic logic under uncertainty," in thethirteenth Australasian symposium on Theory of computing, Darlinghurst, Australia, 2007, vol. 65, pp. 101-110: Computer Society.
[12]      J. Tang, Y. Chang, C. Aggarwal, and H. Liu, "A survey of signed network mining in social media," ACM Computing Surveys (CSUR), vol. 49, no. 3, p. 42, 2016.
[13]      W. Sherchan, S. Nepal, and C. Paris, "A survey of trust in social networks," ACM Computing Surveys, vol. 45, no. 4, 2013.
[14]      J. Golbeck and J. Hendler, " Inferring binary trust relationships in web-based social networks," ACM Transactions on Internet Technology, vol. 6, no. 4, 2006.
[15]      C. N. Ziegler and G. Lausen, "Spreading activation models for trust propagation," in IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE’04), Los Alamitos, CA, 2004, pp. 83–97: IEEE Computer Society.
[16]      U. Kuter and J. Golbeck, "Sunny: A new algorithm for trust inference in social networks using probabilistic confidence models," in National Conference on Artificial Intelligence, British Columbia, Canada, 2007, pp. 1377–1382: AAAI Press.
[17]      S. Kim and S. Han, "The method of inferring Trust in Web-based social Network using Fuzzy logic," in International Workshop On Michine Intelligence Research, 2009, pp. 140-144.
[18]      Y. Ma, L. Hongwei, G. Zaobin, and Z. Yizhu, "Trust Inference Path Search Combining Community Detection and Ant Colony Optimization," Springer International Publishing, pp. 687-698, 2014.
[19]      H. Shakeri and A. Ghaemi Bafghi, "A layer model of a confidence-aware trust management system," International Journal of Information Science and Intelligent System, vol. 3, 2014.
[20]      B. D. Ripley, Pattern Recognition And Neural Networks. Cambridge university press, 2007.
[21]      I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools And Techniques. Morgan Kaufmann, 2016.
[22]      X.-S. Yang and M. Karamanoglu, "Swarm intelligence and bio-inspired computation: an overview," in Swarm Intelligence and Bio-Inspired Computation: Elsevier, 2013, pp. 3-23.
[23]      A. Fernandez, V. Lopez, M. J. del Jesus, and F. Herrera, "Revisiting evolutionary fuzzy systems: Taxonomy, applications, new trends and challenges," Knowledge-Based Systems, vol. 80, pp. 109-121, 2015.
[24]      S. Karakatič and V. Podgorelec, "A survey of genetic algorithms for solving multi depot vehicle routing problem," Applied Soft Computing, vol. 27, pp. 519-532, 2015.
[25]      K. Sastry, D. E. Goldberg, and G. Kendall, "Genetic algorithms," in Search methodologies: Springer, 2014, pp. 93-117.
[26]      عباس صابری نوقابی و نادر هاتفی ترشیزی، «تنظیم بهینه رله دیستانس با در نظر گرفتن عدم قطعیت ها»، مجله مهندسی برق دانشگاه تبریز، جلد ۴۶، شماره ۱، 231-222، 1395.
[27]      V. Pieterse and P. E. Black, Dictionary of Algorithms and Data Structures, 2 ed., 2008. [Online]. Available: http://www.nist.gov/dads/HTML/allSimplePaths.html.
[28]      فرید کربلایی، حمیدرضا شعبانی و رضا ابراهیمپور، «ارزیابی برون‌خط پایداری گذرا به وسیله تعیین دقیق CCT با استفاده از  شبکه عصبی با ورودی‌های مبتنی بر توابع انرژی»، مجله مهندسی برق دانشگاه تبریز، جلد ۴۶، شماره ۱، 285-278، 1395.

Files

Share

How to cite

Statistics