Evaluating geo-social influence in location-based social networks
Published 29 October 2012
Chao Zhang, Lidan Shou, Ke Chen, Gang Chen, Yijun Bei
Citations92
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TL;DR
This paper performs an in-depth analysis of the geo-social correlations among LBSN users at event level, and proposes a unified influence metric that combines a novel social proximity measure named penalized hitting time, with a geographical weight function modeled by power law distribution.
Abstract
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.
Keywords
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