Abstract: According to the potential meaning, the edges in some networks can be divided into positive and negative relationships. When we mark these positive and negative edges with plus and minus signs respectively, a signed network is formed. Signed networks are widespread in sociology, information science, biology and other fields. Nowadays signed networks have become one of research hotspots. Researching on sign prediction problem in signed social networks is valuable to personalized recommendation, abnormal node identification and user clustering in social networks. This paper focus on predicting positive and negative links in signed social networks, and describes domestic and overseas current research status and latest developments. First we introduce the social structural balance theory and status theory. Then we classify several sign prediction algorithms into two categories according to their main ideals: algorithms based on matrix and algorithms based on classification. We introduce the basic idea of these sign prediction algorithms in detail. And then we compare and analyze these algorithms from multiple perspectives such as speed, accuracy, scalability and so on. Finally, we summarize some regularity characteristics and challenges in sign prediction and discuss some possible development directions in signed social networks research.