Document Type : Research Paper
Authors
computer science group, mathematics and computer department, Allameh Tabataba'i university,Tehran,Iran
Abstract
Mobile social networks facilitate connections through mobile devices, and users of these networks can use mobile to access, share and distribute information. With increasing the number of users on social networks, the large amount of shared information and the dissemination of created information cause some problems such as rumor propagation and access to incorrect information. The most powerful tools for validity of received information is based upon the trust value which is assigned by the others. However, considering the subjective and perceptive nature of the concept of trust, the mapping of trust in a computational model is one of the important issues in computing systems of social networks. In addition, there may be various communities on social networks and all users will not be directly connected to each other, which leads to a more complex process of calculating trust. In this research, using user characteristics in social networks, a fuzzy classification approach is proposed, and the trust is computed between users in a class using a computational model. Also trust is gained between users who are not directly connected, using the combination, transition and aggregation processes. By comparing the results, it can be seen that the proposed method recognizes trustworthy people with high precision.
Keywords
منابع
Abdul-Rahman, A., & Hailes, S. (2000). »Supporting trust in virtual communities ». Paper presented at the 33rd Annual Hawaii International Conference on System Sciences.
Blin†, J. M. (1974). Fuzzy Relation in Group Decision Theory (Vol. 4).
Chen, S., Wang, G., & Jia, W. (2014). »k-FuzzyTrust: Efficient trust computation for large-scale mobile social networks using a fuzzy implicit social graph ». www.elsevier.com/locate/ins.
Dumbill, E. (2002). XML Watch: Finding friends with XML and RDF.
Erikson, E. H. (1975). »Childhood and Society ».
Erikson, E. H. (1993). »Childhood and Society ».
Fang, H., Guo, G., & Zhang, J. (2015). »Multi-faceted trust and distrust prediction for recommender systems ». Decision Support Systems, 71, 37–47.
Gambetta, D. (1988). »Trust: Making and Breaking Cooperative Relations » Basil Blackwell.
Golbeck, J. (2006). »Trust on the World Wide Web: A Survey. Foundations and Trends »® in Web Science, 1(2), 131-197.
Golbeck, J. (2009). »Trust and nuanced profile similarity in online social networks ». ACM Transactions on the Web (TWEB), 3(4).
Guangchi Liu, Q. Y., Honggang Wang, Xiaodong Lin, Mike P. Wittie. (2014). »Assessment of multi-hop interpersonal trust in social networks by Three-Valued Subjective Logic ». Paper presented at the IEEE Conference on Computer Communications.
Guojun Wang, W. J., Jie Wu, Zhengli Xiong. (2014). »Fine-Grained Feature-Based Social Influence Evaluation in Online Social Networks ». IEEE Transactions on Parallel and Distributed Systems, 25(9), 2286 - 2296.
Haibin Zhang, Y. W., Xiuzhen Zhang, Ee-Peng Lim. (2015). »ReputationPro: The Efficient Approaches to Contextual Transaction Trust Computation in E-Commerce Environments ». ACM Transactions on the Web (TWEB), 9(1).
Hao, F., Min, G., Lin, M., Luo, C., & Yang, L. T. (2013). »MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks ». IEEE Transactions on Parallel and Distributed Systems, 25(11), 2944 - 2955.
Hoffman, K., Zage, D., & Nita-Rotaru, C. (2009). »A survey of attack and defense techniques ». ACM Computing Surveys (CSUR), 42(1).
Hua Ma, Z. H. (2014). »Cloud service recommendation based on trust measurement using ternary interval numbers ». Paper presented at the 2014 International Conference on Smart Computing.
Huberman, B. A., Romero, D. M., & Wu, F. (2008). »Social networks that matter: Twitter under the microscope ». First Monday, 14(1).
Jiang, W., Wang, G., & Wu, J. (2014). »Generating trusted graphs for trust evaluation in online social networks ». Future Generation Computer Systems, 31, 48–58.
Kim, M., & Park, S. O. (2013). »Group affinity based social trust model for an intelligent movie recommender system ». Multimedia Tools and Applications, 64(2), 505-516. doi:10.1007/s11042-011-0897-8
Marsh, S. P. (1994). Formalising trust as a computational concept. (Ph.D.), University of Stirling.
Massa, P. (2007). »A Survey of Trust Use and Modeling in Real Online Systems Trust E-services » (pp. 51-83).
Mucheol Kim, Jiwan Seo, Sanghyun Noh, & Han, S. (2011). »Identity management-based social trust model for mediating information sharing and privacy enhancement ». doi:10.1002/sec.379.
Ries, S. (2007). »Certain trust: a trust model for users and agents ». Paper presented at the Proceedings of the 2007 ACM symposium on Applied computing.
Rino Falcone, G. P., Cristiano Castelfranchi. (2003). »A Fuzzy Approach to a Belief-Based Trust Computation ». Trust, Reputation, and Security: Theories and Practice, 73-86.
Ruan, Y., & Durresi, A. (2016). »A survey of trust management systems for online social communities - Trust modeling, trust inference and attacks ». Knowledge-Based Systems, 106(3), 150-163
Ruohomaa, S., & Kutvonen, L. (2005). »Trust Management Survey ». Paper presented at the International Conference on Trust Management.
Sepandar D. Kamvar, M. T. S., Hector Garcia-Molina. (2003). »The eigentrust algorithm for reputation management in p2p networks ». Paper presented at the WWW '03 Proceedings of the 12th international conference on World Wide Web.
Shambour, Q. (2012). »A trust-semantic fusion-based recommendation approach for e-business applications ». Decision Support Systems, 54(1), 768–780.
Sherchan, W., Nepal, S., & Paris, C. (2013). »A survey of trust in social networks ». ACM Computing Surveys (CSUR), 45(4).
Tavakolifard, M. (2012). On some Challenges for Online Trust and Reputation Systems. (Ph.D), Norges teknisk-naturvitenskapelige universitet
Vibhor Kant, K. K. B. (2013). »Fuzzy Computational Models of Trust and Distrust for Enhanced Recommendations ». International Journal of Intelligent Systems, 28(4), 332–365.
Wang, G., & Wu, J. (2011). »Multi-dimensional evidence-based trust management with multi-trusted paths ». Future Generation Computer Systems, 27(5), 529-538.
Wenjun Jiang, G. W., Jie Wu. (2012). »Generating trusted graphs for trust evaluation in online social networks ». Future Generation Computer Systems, 31, 48–58.
Yan Lindsay Sun, W. Y., Zhu Han, K.J. Ray Liu. (2006). »Information theoretic framework of trust mod- eling and evaluation for ad hoc networks ». IEEE Journal on Selected Areas in Communications, 24(2), 305-317.
Zadeh, L. A. (1991). »Similarity Relations and Fuzzy Orderings ». Information Sciences, (Vol. 3).
Zhang, P., & Durresi, A. (2012). »Trust management framework for social networks ». Paper presented at the IEEE International Conference on Communications (ICC).
Zheng, X., Wang, Y., Orgun, M. A., Liu, G., & Zhang, H. (2014). »Social Context-Aware Trust Prediction in Social Networks ». Service-Oriented Computing, 527-534.
Ziegler, C.-N., & Golbeck, J. (2007). »Investigating interactions of trust and interest similarity ». Decision Support Systems, 43(2), 460–475.
A method for computing trust value in mobile social networks with fuzzy approach
Abstract
Large-scale mobile social networks (MSNs) facilitate connections through mobile devices, and users of these networks can use mobile to access, share and distribute information. With the increasing the number of users on social networks, the large amount of shared information and the dissemination of information has created challenges for users. One of these challenges is the trust of users to each other. Trust can play an important role in the users' decision-making in social networks, so that most people share their information based on their trust to others, or make decisions relying on information provided by users. However, considering the subjective and perceptive nature of the concept of trust, the mapping of trust in a computational model is one of the important issues in computing systems of social networks. In addition, there may be various communities on social networks and all users will not be directly connected to each other, which leads to a more complex process of calculating trust. In this research, using user characteristics in social networks, a fuzzy classification approach is proposed, and the trust is computed between users in a class using a computational model. Also trust is gained between users who are not directly connected, using the combination, transition and aggregation processes. By comparing the results, it can be seen that the proposed method recognizes trustworthy people with high precision.
Keywords: Fuzzy classification, Fuzzy logic, Mobile social networks, Trust, Trust computing model
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