Social media sites are now becoming very important platforms for product promotion or marketing campaigns. Therefore, there is broad interest in determining ways to guide a site to react more positively to a product with a limited budget. However, the practical significance of the existing studies on this subject is limited for two reasons. First, most studies have investigated the issue in oversimplified networks in which several important network characteristics are ignored. Second, the opinions of individuals are modeled as bipartite states(e.g., support or not) in numerous studies, however, this setting is too strict for many real scenarios. In this study, we focus on social trust networks(STNs), which have the significant characteristics ignored in the previous studies. We generalized a famed continuous-valued opinion dynamics model for STNs, which is more consistent with real scenarios. We subsequently formalized two novel problems for solving the issue in STNs. Moreover, we developed two matrix-based methods for these two problems and experiments on real-world datasets to demonstrate the practical utility of our methods.

中文翻译：

---

社会信任网络中的意见最大化

社交媒体网站现在正成为产品推广或营销活动的非常重要的平台。因此，在确定如何引导站点对预算有限的产品做出更积极的反应方面存在广泛的兴趣。然而，由于两个原因，现有研究在该主题上的实际意义受到限制。首先，大多数研究都调查了过度简化网络中的问题，其中忽略了几个重要的网络特征。其次，在许多研究中，个人的意见被建模为二分状态（例如，支持与否），但是，这种设置对于许多实际场景来说过于严格。在这项研究中，我们专注于社会信任网络（STN），它具有先前研究中忽略的显着特征。我们为 STN 推广了一个著名的连续值意见动态模型，更符合真实场景。我们随后将两个新问题形式化，以解决 STN 中的问题。此外，我们针对这两个问题开发了两种基于矩阵的方法，并在现实世界的数据集上进行了实验，以证明我们方法的实用性。