Abstract: With the increasing popularity of online social network platforms, users tend to join multiple social networks to enjoy diverse network services. User alignment across social networks aims to seek the same natural person from many virtual accounts on different social platforms. It has become a hot research topic in social media over last years, since it plays a fundamental role in a wide range of research areas, such as cross-network recommendation, link prediction and information diffusion. This paper reviews the main achievements of user alignment across social networks in recent years, and provides options for further research through classifying and summarizing the existing methods. Specifically, we first formally define user alignment problem, then sum up a research framework for user alignment problem, and investigates various available methods and research progress from data preprocessing, candidate generation, labeled data acquisition, feature extraction and alignment algorithm. The mainstream user alignment methods are described in detail from the perspective of rule-based and statistics-based. Later, some available data sets and evaluation metrics are briefly introduced. Finally, we discussed the unsolved problems and future research directions for user alignment across social networks.