With the development of mobile Internet, more and more individuals and institutions tend to express their views on certain things (such as software and music) on social platforms. In some online social network services, users are allowed to label users with similar interests as “trust” to get the information they want and use “distrust” to label users with opposite interests to avoid browsing content they do not want to see. The networks containing such trust relationships and distrust relationships are named signed social networks (SSNs), and some real-world complex systems can be also modeled with signed networks. However, the sparse social relationships seriously hinder the expansion of users’ social circle in social networks. In order to solve this problem, researchers have done a lot of research on link prediction. Although these studies have been proved to be effective in the unsigned social network, the prediction of trust and distrust in SSN has not achieved good results. In addition, the existing link prediction research does not consider the needs of user privacy protection, so most of them do not add privacy protection measures. To solve these problems, we propose a trust-based missing link prediction method (TMLP). First, we use the simhash method to create a hash index for each user. Then, we calculate the Hamming distance between the two users to determine whether they can establish a new social relationship. Finally, we use the fuzzy computing model to determine the type of their new social relationship (e.g., trust or distrust). In the paper, we gradually explain our method through a case study and prove our method’s feasibility.

中文翻译：

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带有隐私保护的签名社交网络中基于信任的缺失链接预测

随着移动互联网的发展，越来越多的个人和机构倾向于在社交平台上表达对某些事物（例如软件和音乐）的看法。在某些在线社交网络服务中，允许用户将兴趣与“信任”相似的用户标记为获取他们想要的信息，并使用“不信任”为具有相反兴趣的用户标记以避免浏览他们不想看到的内容。包含此类信任关系和不信任关系的网络称为签名社交网络（SSN），某些现实世界中的复杂系统也可以使用签名网络进行建模。然而，稀疏的社会关系严重阻碍了社交网络中用户社交圈的扩大。为了解决这个问题，研究人员对链路预测进行了大量研究。尽管这些研究已被证明在未签名的社交网络中是有效的，但对SSN中信任和不信任的预测仍未取得良好的结果。另外，现有的链接预测研究没有考虑用户隐私保护的需求，因此大多数都没有增加隐私保护措施。为了解决这些问题，我们提出了一种基于信任的缺失链接预测方法（TMLP）。首先，我们使用simhash方法为每个用户创建一个哈希索引。然后，我们计算两个用户之间的汉明距离，以确定他们是否可以建立新的社交关系。最后，我们使用模糊计算模型来确定其新的社会关系的类型（例如，信任或不信任）。在本文中，我们通过案例研究逐步说明了我们的方法，并证明了该方法的可行性。