Region Aware Influence Maximization in Signed Social Network Using PR Quadtree

A signed social network (SSN) is an association of people with common interest and pursuit. The positive and negative notations are used to represent the relationship between nodes. An influence maximization problem identifies seed nodes that eventually helps in spreading information across the network. Geographical preference of a user is indispensable for some applications such as marketing, branding, and E-commerce. Therefore, a location-aware influence maximization improves the performance of such applications. However, the current methodology using Point-tree (P-tree), Region-tree (R-tree) and Point-Region tree (PR-tree), propagate the information with a considerable time overhead. We address this problem by introducing PR-Quadtree index structure for implementing the maximization algorithm. It is observed that our algorithm facilitates for the optimal selection of seed nodes from the targeted region in order to maximize the influence.