Full Text:   <520>

Summary:  <200>

CLC number: TP391

On-line Access: 2017-12-04

Received: 2016-06-17

Revision Accepted: 2017-04-18

Crosschecked: 2017-11-01

Cited: 0

Clicked: 1627

Citations:  Bibtex RefMan EndNote GB/T7714


Xin Wang


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.10 P.1591-1600


Building trust networks in the absence of trust relations

Author(s):  Xin Wang, Ying Wang, Jian-hua Guo

Affiliation(s):  College of Computer Science and Technology, Jilin University, Changchun 130012, China; more

Corresponding email(s):   xinwangjlu@gmail.com, wangying2010@jlu.edu.cn

Key Words:  Trust network, Sparse learning, Homophily effect, Interaction behaviors

Xin Wang, Ying Wang, Jian-hua Guo. Building trust networks in the absence of trust relations[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1591-1600.

@article{title="Building trust networks in the absence of trust relations",
author="Xin Wang, Ying Wang, Jian-hua Guo",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Building trust networks in the absence of trust relations
%A Xin Wang
%A Ying Wang
%A Jian-hua Guo
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 10
%P 1591-1600
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601341

T1 - Building trust networks in the absence of trust relations
A1 - Xin Wang
A1 - Ying Wang
A1 - Jian-hua Guo
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 10
SP - 1591
EP - 1600
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601341

User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platforms. These issues pose a great challenge for predicting trust relations and further building trust networks. In this study, we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework, bTrust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Agarwal, M., Zhou, B., 2014. Using trust model for detecting malicious activities in Twitter. Int. Conf. on Social Computing, Behavioral-Cultural Modeling, and Prediction, p.207-214.

[2]Au Yeung, C.M., Iwata, T., 2011. Strength of social influence in trust networks in product review sites. Proc. 4th ACM Int. Conf. on Web Search and Data Mining, p.495-504.

[3]Ding, C., Li, T., Jordan, M.I., 2008. Nonnegative matrix factorization for combinatorial optimization: spectral clustering, graph matching, and clique finding. 8th IEEE Int. Conf. on Data Mining, p.183-192.

[4]Forsati, R., Mahdavi, M., Shamsfard, M., et al., 2014. Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Trans. Inform. Syst., 32(4), Article 17.

[5]Guha, R., Kumar, R., Raghavan, P., et al., 2004. Propagation of trust and distrust. Proc. 13th Int. Conf. on World Wide Web, p.403-412.

[6]Guo, G.B., Zhang, J., Yorke-Smith, N., 2015. Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowl.-Based Syst., 74:14-27.

[7]Huang, J., Nie, F.P., Huang, H., et al., 2012. Trust prediction via aggregating heterogeneous social networks. Proc. 21st ACM Int. Conf. on Information and Knowledge Management, p.1774-1778.

[8]Huang, J., Nie, F.P., Huang, H., et al., 2013. Social trust prediction using rank-k matrix recovery. Proc. 23rd Int. Joint Conf. on Artificial Intelligence, p.2647-2653.

[9]Jang, M.H., Faloutsos, C., Kim, S.W., 2014. Trust prediction using positive, implicit, and negative information. Proc. 23rd Int. Conf. on World Wide Web, p.303-304.

[10]Kahanda, I., Neville, J., 2009. Using transactional information to predict link strength in online social networks. Proc. 3rd Int. AAAI Conf. on Weblogs and Social Media, p.74-81.

[11]Kim, Y., Phalak, R., 2012. A trust prediction framework in rating-based experience sharing social networks without a web of trust. Inform. Sci., 191:128-145.

[12]Kuter, U., Golbeck, J., 2007. Sunny: a new algorithm for trust inference in social networks using probabilistic confidence models. Proc. 22nd National Conf. on Artificial Intelligence, p.1377-1382.

[13]Leskovec, J., Huttenlocher, D., Kleinberg, J., 2010. Predicting positive and negative links in online social networks. Proc. 19th Int. Conf. on World Wide Web, p.641-650.

[14]Liben-Nowell, D., Kleinberg, J., 2007. The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol., 58(7):1019-1031.

[15]Liu, H.F., Lim, E.P., Lauw, H.W., et al., 2008. Predicting trusts among users of online communities: an Epinions case study. Proc. 9th ACM Conf. on Electronic Commerce, p.310-319.

[16]Massa, P., Avesani, P., 2005. Controversial users demand local trust metrics: an experimental study on Epinions.com community. Proc. 20th National Conf. on Artificial Intelligence, p.121-126.

[17]McPherson, M., Smith-Lovin, L., Cook, J.M., 2001. Birds of a feather: homophily in social networks. Ann. Rev. Sociol., 27(1):415-444.

[18]Mishra, A., Bhattacharya, A., 2011. Finding the bias and prestige of nodes in networks based on trust scores. Proc. 20th Int. Conf. on World Wide Web, p.567-576.

[19]Nguyen, V.A., Lim, E.P., Jiang, J., et al., 2009. To trust or not to trust? Predicting online trusts using trust antecedent framework. 9th IEEE Int. Conf. on Data Mining, p.896-901.

[20]Nielsen, M., Krukow, K., Sassone, V., 2007. A Bayesian model for event-based trust. Electron. Notes Theor. Comput. Sci., {bf 172}:499-521.

[21]Tang, J.L., Gao, H.J., Liu, H., 2012. mTrust: discerning multi-faceted trust in a connected world. Proc. 5th ACM Int. Conf. on Web Search and Data Mining, p.93-102.

[22]Tang, J.L., Gao, H.J., Hu, X., et al., 2013. Exploiting homophily effect for trust prediction. Proc. 6th ACM Int. Conf. on Web Search and Data Mining, p.53-62.

[23]Wang, D.S., Pedreschi, D., Song, C.M., et al., 2011. Human mobility, social ties, and link prediction. Proc. 17th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.1100-1108.

[24]Wang, Y., Vassileva, J., 2003. Trust and reputation model in peer-to-peer networks. Proc. 3rd Int. Conf. on Peer-to-Peer Computing, p.150-157.

[25]Wang, Y., Li, L., Liu, G.F., 2013. Social context-aware trust inference for trust enhancement in social network based recommendations on service providers. World Wide Web, 18(1):159-184.

[26]Wang, Y., Wang, X., Tang, J.L., et al., 2015a. Modeling status theory in trust prediction. Proc. 29th AAAI Conf. on Artificial Intelligence, p.1875-1881.

[27]Wang, Y., Wang, X., Zuo, W.L., 2015b. Research on trust prediction from a sociological perspective. J. Comput. Sci. Technol., 30(4):843-858.

[28]Wolff, D., Weyde, T., 2014. Learning music similarity from relative user ratings. Inform. Retr., 17(2):109-136.

[29]Ye, J.J., 2006. Constraint qualifications and KKT conditions for bilevel programming problems. Math. Oper. Res., 31(4):811-824.

[30]Zhang, R.C., Mao, Y.Y., 2014. Trust prediction via belief propagation. ACM Trans. Inform. Syst., 32(3), Aritcle 15.

[31]Zheng, X.M., Wang, Y., Orgun, M.A., et al., 2014. Trust prediction with propagation and similarity regularization. Proc. 28th AAAI Conf. on Artificial Intelligence, p.237-243.

[32]Zhu, S.H., Yu, K., Chi, Y., et al., 2007. Combining content and link for classification using matrix factorization. Proc. 30th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.487-494.

[33]Zolfaghar, K., Aghaie, A., 2011. Evolution of trust networks in social web applications using supervised learning. Proc. Comput. Sci., 3:833-839.

[34]Zolfaghar, K., Aghaie, A., 2012. A syntactical approach for interpersonal trust prediction in social web applications: combining contextual and structural data. Knowl.-Based Syst., 26:93-102.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE