Geo-social Influence Spanning Maximization - 2017

PROJECT TITLE :

Geo-social Influence Spanning Maximization - 2017

ABSTRACT:

Influence maximization may be a recent but well-studied downside that helps identify a little set of users that are most likely to “influence” the most number of users in a social network. The problem has attracted a lot of attention because it provides a method to boost promoting, branding, and product adoption. But, existing studies rarely contemplate the physical locations of the users, however location is a vital factor in targeted marketing. In this paper, we tend to propose and investigate the matter of influence maximization in location-aware social networks, or, additional generally, Geo-social Influence Spanning Maximization. Given a query q composed of a locality R, a regional acceptance rate p, and an integer k as a seed selection budget, our aim is to seek out the utmost geographic spanning regions (MGSR). We refer to the present as the MGSR drawback. Our approach differs from previous work as we tend to focus additional on identifying the maximum spanning nation-states inside a part R, rather than simply the quantity of activated users within the given network just like the traditional influence maximization problem [14]. Our research approach will be effectively used for on-line promoting campaigns that depend on the physical location of social users. To handle the MGSR downside, we initial prove NP-Hardness. Next, we tend to gift a greedy algorithm with a one - one=e approximation ratio to solve the problem, and further improve the potency by developing an higher bounded pruning approach. Then, we tend to propose the OIR*-Tree index, that is a hybrid index combining ordered influential node lists with an R*-tree. We tend to show that our index primarily based approach is significantly more economical than the greedy algorithm and also the higher bounded pruning algorithm, especially when k is large. Finally, we have a tendency to evaluate the performance for all of the proposed approaches using three real datasets.

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