Features for link prediction in social networks: A comprehensive study

With the development of social media websites, more and more users start to show their attitudes and emotions to each other. Some of these interactions can be represented as links with sign values(positive or negative). In this paper, a unified method is proposed for link prediction and feature analysis. This paper focuses on the data from social media websites and tries to find the features that determine the sign value mostly. Based on the features extracted from the users´ self statuses and from their relationships with neighbors, our method can predict the links´ values with high accuracy. By analyzing the models generated over different datasets, our experiments find out the common determining features for link prediction. Based on our results, advices on how to predict links´ values and get more positive links in future are given to users.