Document Type: Original Research Paper


1 student of ACRCE Khozestan

2 Depatment of Computer, Central Tehran Branch, Islamic Azad University, Tehran, Iran


Today, e-commerce has occupied a large volume of economic exchanges. It is known as one of the most effective business practices. Predicted trust which means trusting an anonymous user is important in online communities. In this paper, the trust was predicted by combining two methods of multiplex network and community detection. In modeling the network in terms of a multiplex network, the relationships between users were different in each layer and each user had a rank in each layer. Then, the ratings of two layers including the weight of each layer were aggregated and four effective features of the Trust were achieved. Then, the network was divided into overlapping groups via community detection’ algorithms, each group representative was considered as the community centers and other features were extracted through similar comments. At the end, 48J decision tree algorithm was used to advance the work. The proposed method was assessed on Epinions data set and accuracy of trust was 96%.


Main Subjects

1. Wang Y., Wang X., Tang J., Zuo W., Cai G. 2015: Modeling Status Theory in Trust Prediction. The AAAI Conference on Artificial Intelligence (AAAI).
2. Chakraborty P., Karform, S. 2012: Designing trust propagation algorithms based on simple multiplicative strategy for Social Networks. Procedia Technology, Vol. 6, pp.534-539.
3. Tang, J., Gao, H., Hu, X., Liu, H. 2013: Exploiting homophily effect for trust prediction. In WSDM., ACM, pp. 53-62.
4. Javari, A., Jalili, M. 2014: Cluster-Basad Collaborative Filtering for Sign Prediction on Intelligent Systems and Technologi., (TIST), vol. 5,no. 2,p.24.
5. Beigi, Gh., Jalili, M., Alvari, H., Sukthankar, G. 2014: Leveraging Community Detection for Accurate Trust Prediction. in Proceedings of the sixth ASE international conference on Social Computing, Stanford, CA, USA.
6. Torkzadeh Mahani, R, Analoui, M. 2015: Trust Prediction in Multiplex Networ. International conference on Knowledge-Based Engineering and Innovation (KBEI), pp263-268.
7. Kasprzak, R. 2012: Diffusion in networks. Jornal of Telecommunications and Information Technology, pp.99-106.
8. Jierui, Xie, Boleslaw, K. Szymanski and Xiaoming Liu, 2011” SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process”, IEEE ICDM workshop on DMCCI.
9. Raghavan, U. N., Albert, R., Kumara, S. 2007: Near linear time algorithm to detect community structures in large-scale networks. Physical Review. E, vol. 76, p. 036106.
10. Combe, D., Largeron, C,. Egyed-Zsigmond, E., Géry, M. 2010: A comparative study of social network analysis tools. International Workshop on Web Intelligence and Virtual Enterprises.
11. Alvari, H., Hashemi, S., Hamzeh, A. 2011: Detecting overlapping communities in social networks by game theory and structural equivalence concept. Artificial Intelligence and Computational Intelligence, pp. 620–630.
12. Waltman, L., van Eck, N. J. 2013: A smart local moving algorithm for large-scale modularitybased community detection. CoRR, vol. abs/1308.6604.
13. Zheng, X. 2015: Trust Prediction in Online Social Networks. A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computing Faculty of Science and Engineering Macquarie University.
14. Zheng, X., Wang, Y., Orgun, M. A., Zhong, Y., Liu. G. 2014: Trust prediction with propagation and similarity regularization. In 28th AAAI Conference on Artificial Intelligence, pages 237–243, Quebec City, Quebec, Canada.
15. Zhang, H., Wang, Y., Zhang, X. 2013: The approaches to contextual transaction trust computation in e-commerce environments. Security and Communication Networks.
16. Yao, Y., Tong, H., Yan, X., Xu, F., Lu J. 2013: Multi-aspect + transitivity + bias: An integral trust inference model. IEEE Transactions on Knowledge and Data Engineering.
17. Leskovec, J., Huttenlocher D., Kleinberg, J. 2010:Predicting positive and negative links in online social networks. in In Predictings of the 19th international conference on World Wide Web.