论文标题

社会信任网络中的意见最大化

Opinion Maximization in Social Trust Networks

论文作者

Xu, Pinghua, Hu, Wenbin, Wu, Jia, Liu, Weiwei

论文摘要

社交媒体网站现在已成为产品推广或营销活动的非常重要的平台。因此,确定指导网站对预算有限的产品更积极地做出反应的方法有广泛的兴趣。但是,现有研究对该主题的实际意义受到限制,原因有两个。首先,大多数研究都调查了过度简单的网络中的问题,在这些网络中,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两分国家(例如,支持与否),但是对于许多实际情况来说,这种环境太严格了。在这项研究中,我们专注于社会信任网络(STN),这些网络具有以前的研究中忽略的重要特征。我们概括了一个著名的连续价值动态模型,该模型与实际场景更一致。随后,我们正式化了两个新的问题,以解决STN中的问题。此外,我们为这两个问题开发了两种基于矩阵的方法,并在现实世界数据集上进行了实验,以证明我们方法的实际实用性。

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.

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