Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method.

中文翻译：

---

使用异构网络的社会信任预测

随着社交网站的日益普及，在线用户更多地依赖可信度信息来做出决策、提取和过滤信息，以及标记和建立与其他用户的联系。然而，此类社交网络数据往往存在严重的数据稀疏性，无法为用户提供足够的信息。因此，信任预测已成为社交网络研究中的一个重要课题。传统方法主要基于探索信任图拓扑本身。然而，社会学研究和我们的生活经验表明，处于同一社交圈的人往往表现出相似的行为和品味。为了利用辅助信息进行信任预测，挑战就变成了转移什么以及如何转移。在本文中，我们通过聚合异构社交网络来解决这个问题，并提出了一种新的联合社交网络挖掘（JSNM）方法。我们新的联合学习模型探索了相关图之间的用户组级相似性，同时学习了单个图结构；因此，可以利用来自多个社交网络的共享结构和模式来增强预测任务。因此，我们不仅提高了目标图中的信任预测，还促进了辅助图中的其他信息检索任务。为了优化提出的目标函数，我们使用替代技术将目标函数分解为几个可管理的子问题。我们进一步引入辅助函数来解决具有严格证明收敛性的优化问题。已经在合成数据和真实世界数据上进行了广泛的实验。所有实证结果都证明了我们方法的有效性。