Evaluating geo-social influence in location-based social networks

The emerging location-based social network (LBSN) services not only allow people to maintain cyber links with their friends, but also enable them to share the events happening on them at different locations. The geo-social correlations among event participants make it possible to quantify mutual user influence for various events. Such a quantification of influence could benefit a wide spectrum of real-life applications such as targeted advertising and viral marketing. In this paper, we perform an in-depth analysis of the geo-social correlations among LBSN users at event level, based on which we address two problems: user influence evaluation and influential events discovery. To capture the geo-social closeness between LBSN users, we propose a unified influence metric. This metric combines a novel social proximity measure named penalized hitting time, with a geographical weight function modeled by power law distribution. We propose two approximate algorithms, namely global iteration (GI) and dynamic neighborhood expansion (DNE), to efficiently evaluate user influence with tight theoretical error bounds. We then adopt the sampling technique and the threshold algorithm to support efficient retrieval of top-K influential events. Extensive experiments on both real-life and synthetic LBSN data sets confirm that the proposed algorithms are effective, efficient, and scalable.