As mobile devices are becoming ubiquitous nowadays, the geographical activities and interactions of human beings can be easily recorded and accessed. Each mobile individual can belong to an online social network. Unfortunately, the underlying online social relationships are hidden and only available to service providers. Acquiring the social network of mobile users would enrich lots of mobile applications, such as friend recommendation and energy-saving mobile database management. In this work, we propose to infer online social ties using purely offline geographical activities of users, such as check-in records and spatial meeting events. To tackle the problem, we devise a novel inference framework, O2O-Inf, which consists of two components, Feature Modeling and Link Inference. Feature modeling is to characterize both direct and indirect geographical interactions between nodes from co-location and graph features. Link inference aims to infer the social ties based on a small set of observed social links, and the idea is that pairs of nodes sharing similar geographical behaviors have the same tendency of linkage (i.e., either being friends or non-friends). Experiments conducted on a Gowalla location-based social network and a Meetup event-based social network exhibit a satisfying performance in comparison to state-of-the-art prediction methods under the settings of offline-to-online network inference and geo-link prediction.
|Journal||ACM Transactions on Intelligent Systems and Technology|
|Publication status||Published - 2019 Jan|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Artificial Intelligence