In the age of digital interaction, person-to-person relationships existing on social media may be different from the very same interactions that exist offline. Examining potential or spurious relationships between members in a social network is a fertile area of research for computer scientists -- here we examine how relationships can be predicted between two unconnected people in a social network by using area under Precison-Recall curve and ROC. Modeling the social network as a signed graph, we compare Triadic model,Latent Information model and Sentiment model and use them to predict peer to peer interactions, first using a plain signed network, and second using a signed network with comments as context. We see that our models are much better than random model and could complement each other in different cases.

翻译：在数字互动的时代,社交媒体上存在的人际关系可能不同于离线的完全相同的互动。研究社交网络成员之间的潜在或虚假关系是计算机科学家研究的肥沃领域 -- -- 在这里,我们研究如何利用Precison-Recall曲线和ROC之下的区域预测社交网络中两个互不相连的人之间的关系。我们将社交网络建模成一个签名图,我们比较Triadic模型、Latent Information和感应模型,并用它们来预测同行之间的互动,首先使用一个简单签名的网络,其次使用一个签名的网络作为背景评论。我们发现我们的模型比随机模型好得多,在不同情况下可以互为补充。