An Adaptive neuro-fuzzy Inference System to Evaluate Trustworthiness of Users in a Social Network
محورهای موضوعی : Computer EngineeringMohammadMahdi Shafiei 1 , Hossein Shirgahi 2 , Homayun Motameni 3 , Behnam Barzegar 4
1 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
2 - Department of Computer Engineering, Jouybar Branch, Islamic Azad University, Jouybar,Iran
3 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran.
4 - Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran
کلید واژه: Trust, Adaptive neuro-fuzzy inference system, social network,
چکیده مقاله :
In recent years, the emergence of various social networks has led to the growth of social network users. However, activity in such networks depends on the level of trust that users have in each other. Therefore, trust is essential and important issue in these networks, especially when users interact with each other. In this article, we examine this issue and provide a method to evaluate it. It is not easy to measure the accuracy of trust for users who interact with social networks. Here, interactions are virtual. In this article, we have used the adaptive neuro-fuzzy inference system to evaluate trustworthiness by considering different personality attributes of users such as reliability, availability, interest, patience and adaptability. Using these features as input and based on the adaptive neuro-fuzzy inference system, we evaluated the trustworthiness of users in social network. The proposed adaptive neuro-fuzzy inference system is expandable because in this system, trust can be defined as a set of one or more personality attributes. Epinions social network dataset is also used to simulate and validate the proposed method. In the proposed method, the absolute mean value of error is less than 0.0095 and the value of F-score is more than 0.9884. Based on the obtained results and compared to the previous methods, the proposed adaptive neuro-fuzzy inference system shows an acceptable accuracy for evaluating the trustworthiness of users.
In recent years, the emergence of various social networks has led to the growth of social network users. However, activity in such networks depends on the level of trust that users have in each other. Therefore, trust is essential and important issue in these networks, especially when users interact with each other. In this article, we examine this issue and provide a method to evaluate it. It is not easy to measure the accuracy of trust for users who interact with social networks. Here, interactions are virtual. In this article, we have used the adaptive neuro-fuzzy inference system to evaluate trustworthiness by considering different personality attributes of users such as reliability, availability, interest, patience and adaptability. Using these features as input and based on the adaptive neuro-fuzzy inference system, we evaluated the trustworthiness of users in social network. The proposed adaptive neuro-fuzzy inference system is expandable because in this system, trust can be defined as a set of one or more personality attributes. Epinions social network dataset is also used to simulate and validate the proposed method. In the proposed method, the absolute mean value of error is less than 0.0095 and the value of F-score is more than 0.9884. Based on the obtained results and compared to the previous methods, the proposed adaptive neuro-fuzzy inference system shows an acceptable accuracy for evaluating the trustworthiness of users.
[1] P. Christoph,A.Rieger, A. Gaines and M. Gibbons, “Trust and respect in the patient-clinician relationship: preliminary development of a new scale”. BMC Psychology, 2019.
[2] W. Brunotte, A. Specht, L. Chazette and K. Schneider, “Privacy explanations – A means to end-user trust.” Journal of Systems and Software, Elsevier, 2023.
[3] V. Rahmati, S. Siadat, F. Noorani, “The enhancement of online social network security by detecting and preventing fake accounts through machine learning,” Scientific Journal of Electronic and Cyber Defense ,2022,Vol. 10, No. 1, pp. 85-97.
[4] S. Singh, J. Sidhu, “An Approach for Determining Trust-worthiness of Individuals in a Web-Based Social Network”. Arabian Journal for Science and Engineering,2016, 41, (2), pp. 461-477.
[5] D.J. Watts, “Social Networks as a Phenomenon of the Information Society”,Journal of Optimization in Industrial Engineering, 2021,pp. 17-24.
[6] P. Govindaraj, N. Jaisankar,” A Review on Various Trust Models in Cloud Environment,” Journal of Engineering Science andTechnology Review ,2017, p.p. 213-219.
[7] Y. Xu, Z. Gong, J. Forrest, E. Herrera, E,” Trust propagation and trust network evaluation in social networks based on uncertainty theory,”Knowledge-Based Systems, Elsevier,2021,Volume234.
[8] C. Mao, C. Xu, Q. He, “A cost-effective algorithm for inferring the trust between two individuals in social networks,” Knowledge-Based Systems. Elsevier, 2018.
[9] N. Li, V. Varadharajan, S. Nepal, “Context-Aware Trust Management System for IoT Applications with Multiple Domains,” IEEE 39th International Conference on Distributed Computing Systems, 2019.
[10] A. Alhussain, H. Kurdi, “EERP: An enhanced EigenTrust algorithm for reputationmanagement in peer-to-peer networks,” The 5th International Symposium on Emerging Information, Communication and Networks, Procedia Computer Science 141, 2018, pp. 490–49.
[11]T. Wen, “Evaluating the Vulnerability of Communities inSocial Networks by Gravity Model,” JournalofLatexClassFiles, 2019, VOL. 14, NO. 8.
[12] X. Wang, Y. Yin, J. Deng, Z. Xu, “Influence of trust networks on the cooperation efficiency of PPP projects: moderating effect of opportunistic behavior, “Journal of Asian ArchitectureandBuildin Engineering, 2021VOL. 22, NO. 4, pp. 2275–2290.
[13] J. Jiang, H. Wang, W. Li,“ A Trust model based on a time decay factor for use in social networks,”Computers & Electrical Engineering, Elsevier, 2020.
[14] M. Shafiei, H. Shirgahi, H. Motameni, and B. Barzegar,"A Fuzzy System for Evaluating Trustworthiness of Users in A Social Network,”IIUM Engineering Journal,2022, Vol. 23, No. 2.
[15] M. Mohanapriya and I. Krishnamurthi, “Trust based DSR routingprotocol for mitigating cooperative black hole attacks in ad hocnetworks,” Arabian Journal for Science and Engineering,2014,Vol. 39, No. 3, pp. 1825–1833.
[16] H. Shirgahi, M. Mohsenzadeh, and H.H. SeyyedJavadi, “Trust estimation of the semantic webusing Semantic web clustering,” Journal of Experimental &Theoretical Artificial Intelligence,2016,Vol. 29, No. 3, pp. 537-556.
[17] L. Sbaffi, J. Rowley,” Trust and Credibility in Web-Based Health Information: A Review and Agenda for Future Research,”Journal of Medical Internet Research , 2017.
[18]J. Peng, X. Chen, C. Tian, Z. Zhang, H. Song, F. Dong,” Picture fuzzy large-scale group decision-making in a trust- relationship-based social network environment,” Information Sciences, Elsevier,2022,pp. 1675-1701.
[19] R. Kong, X. Tong,” Dynamic Weighted Heuristic Trust PathSearch Algorithm,” IEEE Access ,Vol.8, 2020.
[20] Y. Gong, L. Chen, T. Ma,” A Comprehensive Trust Model Based on Social Relationship andTransaction Attributes,” Security and Communication Networks, Hindawi, 2020.
[21] M. Cheng, S. Nazarian, P. Bogdan, “There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks,” National Library of Medicine, 2020.
[22] AH. Danesh and H. Shirgahi, “Predicting trust in a social network based on structuralsimilarities using a multi-layerd perceptron neural network,” IIUM Engineering Journal,2020, Vol. 22, No. 1, pp.103-117.