The quality of a location-based social network (LBSN) is mainly related to the granularity of information on the users' location. When LBSN is built using stay points, it presents much more information since GPS logs convey more users' mobility information. However, the main challenge in building LBSN using stay points is to define local-vertices. This problem is known as local-entity resolution. This local-vertices could represent venues with semantic information like parks, restaurants, among others. The most common way to resolve local-entity is by applying clustering algorithms to group nearby stay points into local-vertices. However, in this case, only geographic information is used, which makes it very difficult to separate geographically close venues into distinct local-vertices. This paper addresses this gap and presents a novel approach that uses the coarsening stage of a multilevel optimization scheme to build LBSNs by using stay points. The experimental evaluation carried out indicates that our approach has advantages compared to usual clustering methods to represent real-world features.

基于位置的社交网络（LBSN）的质量主要与用户位置信息的粒度有关。当使用停留点构建LBSN时，由于GPS日志可传达更多用户的移动性信息，因此它会显示更多信息。但是，使用停留点构建LBSN的主要挑战是定义局部顶点。此问题称为本地实体解析。该局部顶点可以表示具有语义信息的场所，例如公园，饭店等。解决本地实体的最常见方法是应用聚类算法将附近的停留点分组为本地顶点。然而，在这种情况下，仅使用地理信息，这使得将地理上接近的场所划分为不同的局部顶点非常困难。本文解决了这一差距，并提出了一种新颖的方法，该方法使用多级优化方案的粗化阶段通过使用停留点来构建LBSN。进行的实验评估表明，与代表真实世界特征的常规聚类方法相比，我们的方法具有优势。