信任网络中多维信任序列模式挖掘方法研究

Multi-dimensional Trust Sequential Patterns Mining Algorithm in Trust Networks

现有信任网络研究大多侧重于信任的推理及聚合计算,缺乏对实体重要性及其关联性分析,为此该文提出一种多维信任序列模式(Multi-dimensional Trust Sequential Patterns,MTSP)挖掘算法。该算法包括频繁信任序列挖掘和多维模式筛选两个处理过程,综合考虑信任强度、路径长度和实体可信度等多维度因素,有效地挖掘出信任网络中的频繁多维信任序列所包含的重要实体及其关联结构。仿真实验表明该文所提MTSP算法的挖掘结果全面、准确地反映了信任网络中重要信任实体关联性及其序列结构特征。

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.