Adversity-based Social Circles Inference via Context-Aware Mobility

Abstract

The ubiquity of mobile devices use has generated huge volumes of location-aware contextual data, providing opportunities enriching various location-based social network (LBSN) applications – e.g., trip recommendation, ride-sharing allocation and taxi demand prediction etc. Trajectory-based social circle inference (TSCI), which aims at inferring the social relationships among users based on the human mobility data, has received great attention in recent years due to its importance in many LBSN applications. However, existing solutions suffer from three key challenges, including (1) lack of modeling contextual feature in user check-ins; (2) cannot capture the structural information in user motion patterns; (3) and fail to consider the underlying mobility distribution. In this paper, we propose a novel framework ASCI-CAM (Adversity-based Social Circles Inference via Context-Aware Mobility) to address the above challenges. ASCI-CAM is a graph-based model taking into account the contextual information associated with check-ins which, combined with an attentive auto-encoder, allows for semantic trajectory representation. We regularize the learned trajectory embedding with an adversarial learning procedure, which allows us to better understand the user mobility patterns and personalized trajectory distribution. Our extensive experiments on real-world mobility datasets demonstrate that our model achieves significant improvement over the state-of-the-art baselines.