RESUMEN
With the development of mobile networks, social networking plays an increasingly important role in people's daily life. User identification, which aims to match user cross-site accounts, has been becoming an important issue for user supervision and recommendation system design in social networks. At present, many different user identification methods have emerged, such as DPLink, HFUL, etc. However, compared with the continuous development of user identification methods, the open-source work of datasets is very slow, and the datasets of most of the work are not public. The shortage of datasets has greatly hindered the development of this research field. At present, the academic urgently needs a large-scale social network user linkage dataset. In this paper, we publicize a new social network user linkage dataset, XSiteTraj v1.0 [2]. This dataset has good spatio-temporal coverage, including more than 27,000 users and more than one million check-in records from all over the world crawled from Facebook, Foursquare, and Twitter. Our dataset labels the identical users from different social websites, and each check-in record includes a timestamp, point of interest (PoI), and latitude and longitude of PoI. Through our dataset, we can conduct research on user behaviour habits and apply the dataset to the experiments and evaluation of social network user identification and other algorithms.