KMUL: A User Identity Linkage Method across Social Networks Based on Spatiotemporal Data

With the increasing availability of spatiotemporal data, user identity linkage across social networks based on spatiotemporal data has attracted more and more attention. The existing methods have some problems, such as trajectory processing is not suitable for sparse data, grid based processing leads to information loss and anomaly, similarity threshold and other parameters are difficult to determine. To solve the above problems, we propose a k-means clustering based method KMUL to solve the problem of user identity linkage based on spatiotemporal data. According to the sparsity, heterogencity and imbalance of spatiotemporal data in social networks, this method can represent the user identity as the form of cluster centers, and effectively link the user identity by calculating the distance between the cluster center representations. We compare this method with several state-of-the-art user identity linkage methods based on spatiotemporal data on real datasets, and the results show that this method outperforms the baseline methods in terms of effectiveness and efficiency.