OpinionWalk: An efficient solution to massive trust assessment in online social networks
OpinionWalk: An efficient solution to massive trust assessment in online social networks
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DOI：
10.1109/infocom.2017.8057106
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发表时间：
2017-05
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影响因子：
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通讯作者：
Guangchi Liu;Qi Chen;Q. Yang;B. Zhu;Honggang Wang;Wei Wang
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文献类型：
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作者：
Guangchi Liu;Qi Chen;Q. Yang;B. Zhu;Honggang Wang;Wei Wang
在线社交网络（OSN）中的大规模信任评估（MTA），即，计算网络中所有用户的可信度在各种OSN相关应用中至关重要。现有的解决方案在解决MTA问题方面要么太慢，要么不准确。我们提出的OpinionWalk算法，准确，有效地进行MTA在OSN。OpinionWalk通过Dirichlet分布对信任进行建模，并使用矩阵来表示用户之间的直接信任关系。从用户的角度来看，其他用户的可信度存储在列向量中，当算法以广度优先搜索的方式“遍历”网络时，该列向量会迭代更新。我们确定MTA中的重叠子问题属性，并证明OpinionWalk是一个更有效的解决方案。使用两个真实世界的数据集（Advogato和Pretty Good Privacy）评估OpinionWalk的准确性和执行时间，并与EigenTrust，TrustRank，MoleTrust，TidalTrust和AssessTrust等基准算法进行比较。实验结果表明，OpinionWalk是一个高效和准确的解决方案，MTA相比，以前的算法。
Massive trust assessment (MTA) in an Online Social Network (OSN), i.e., computing the trustworthiness of all users in the network, is crucial in various OSN-related applications. Existing solutions are either too slow or inaccurate in addressing the MTA problem. We propose the OpinionWalk algorithm that accurately and efficiently conducts MTA in an OSN. OpinionWalk models trust by the Dirichlet distribution and uses a matrix to represent the direct trust relations among users. From the perspective of a user, other users' trustworthiness are stored in a column vector that is iteratively updated when the algorithm “walks” through the network, in a breadth-first search manner. We identify the overlapping subproblems property in MTA and prove OpinionWalk is a more efficient solution. The accuracy and execution time of OpinionWalk are evaluated and compared to benchmark algorithms including EigenTrust, TrustRank, MoleTrust, TidalTrust and AssessTrust, using two real-world datasets (Advogato and Pretty Good Privacy). Experimental results indicate that OpinionWalk is an efficient and accurate solution to MTA, compared to previous algorithms.