A Heterogeneous Graph Embedding Framework for Location-Based Social Network Analysis in Smart Cities

Abstract

In recent years, with the advancement of wireless communication and location acquisition technology in the context of modern smart cities, and the increasing popularity of mobile devices with location capabilities based on Social Internet of Things, we can now easily introduce location services into traditional social networks. How to effectively use these massive data to provide decision support for smart cities is an emerging task. It is necessary to find an efficient way to effectively extract and represent useful information from location-based social networks (LBSNs) by dealing with the data heterogeneity. Against this background, this article proposes a heterogeneous graph embedding framework for LBSN analysis named location based social network embedding (LBSNE). LBSNE framework first constructs the heterogeneous neighborhood of a node by formalizing a metapath-based random walk on LBSNs. Then, it leverages the learned heterogeneous neighborhood sequence to do network embedding by employing a heterogeneous skip-gram model. The effectiveness of the proposed model is evaluated on the tasks of location recommendation and visitor predict on two LBSN datasets.