innovation.gr

The ubiquity of mobile location aware devices and the prolifera-tion of social networks have given rise to Location-Aware Social Networks (LASN), where users form social connections and make geo-referenced posts. The goal of this paper is to identify users that can influence a large number of important other users, within a given spatial region. Returning a ranked list of regionally influen-tial LASN users is useful in viral marketing and in other per-region analytical scenarios. We show that under a general influence prop-agation model, the problem is #P-hard, while it becomes solvable in polynomial time in a more restricted model. Under the more re-strictive model, we then show that the problem can be translated to computing a variant of the so-called closeness centrality of users in the social network, and devise an evaluation method