Multi-dimensional Trust Sequential Patterns Mining Algorithm in Trust Networks
Gong Wei-hua Guo Wei-peng Yang Liang-huai
(School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China)
(Key Laboratory of Visual Media Intelligent Process Technology of Zhejiang Province, Hangzhou 310023, China)
Abstract：The most recent studies in the trust networks focus on the trust inference and aggregation mechanisms, but the issues of correlations between trusted nodes and their structural analysis have not get much attention. To address this weakness, a new Multi-dimensional Trust Sequential Pattern (MTSP) mining algorithm called is proposed, which mainly includes two continuous processes: mining the frequent trust sequences and then filtering the multi-dimensional patterns. And with multiple factors such as trust strength, length of sequences and node credibility taken into account, the algorithm can effectively grab the multi-dimensional frequent trust sequences in the trust networks that imply the correlations between the important nodes as well as their sequence structure in these trust sequences. The simulation experiments show that the results of the proposed MTSP algorithm is able to comprehensively and accurately reflect the characteristics of the important nodes and correlations between them in the trust networks.
2014, Vol. 36 
