TY - GEN
T1 - Optimal social trust path selection in complex social networks
AU - Liu, Guanfeng
AU - Wang, Yan
AU - Orgun, Mehmet A.
PY - 2010
Y1 - 2010
N2 - Online social networks are becoming increasingly popular and are being used as the means for a variety of rich activities. This demands the evaluation of the trust worthiness between two unknown participants along a certain social trust path between them in the social net work. However, there are usually many social trust paths between participants. Thus, a challenging problem is finding which social trust path is the optimal one that can yield the most trustworthy evaluation result. In this paper, we first present a new complex social network structure and a new concept of Quality of Trust (QoT) to illustrate the ability to guarantee a certain level of trustworthiness in trust evaluation. We then model the optimal social trust path selection as a Multi-Constrained Optimal Path (MCOP) selection problem which is NP-Complete. For solving this problem, we propose an efficient approximation algorithm MONTE.K based on the Monte Carlo method. The results of our experiments conducted on a real dataset of social networks illustrate that our proposed algorithm significantly outperforms existing approaches in both efficiency and the quality of selected social trust paths.
AB - Online social networks are becoming increasingly popular and are being used as the means for a variety of rich activities. This demands the evaluation of the trust worthiness between two unknown participants along a certain social trust path between them in the social net work. However, there are usually many social trust paths between participants. Thus, a challenging problem is finding which social trust path is the optimal one that can yield the most trustworthy evaluation result. In this paper, we first present a new complex social network structure and a new concept of Quality of Trust (QoT) to illustrate the ability to guarantee a certain level of trustworthiness in trust evaluation. We then model the optimal social trust path selection as a Multi-Constrained Optimal Path (MCOP) selection problem which is NP-Complete. For solving this problem, we propose an efficient approximation algorithm MONTE.K based on the Monte Carlo method. The results of our experiments conducted on a real dataset of social networks illustrate that our proposed algorithm significantly outperforms existing approaches in both efficiency and the quality of selected social trust paths.
UR - http://www.scopus.com/inward/record.url?scp=84868260050&partnerID=8YFLogxK
M3 - Conference proceeding contribution
AN - SCOPUS:77958608851
SN - 9781577354666
VL - 3
T3 - Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010
SP - 1391
EP - 1398
BT - AAAI-10 / IAAI-10 - Proceedings of the 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference
PB - Association for the Advancement of Artificial Intelligence
CY - Palo Alto, California
T2 - 24th AAAI Conference on Artificial Intelligence and the 22nd Innovative Applications of Artificial Intelligence Conference, AAAI-10 / IAAI-10
Y2 - 11 July 2010 through 15 July 2010
ER -