Community-based seeds selection algorithm for location aware influence maximization

Abstract

In this paper, we study the location aware influence maximization problem, which finds a seed set to maximize the influence spread on targeted users for a given query. In particular, we consider users who have geographical preferences on queries as targeted users. One challenge of the problem is how to find the targeted users and compute their preferences efficiently for given queries. To address this challenge, based on the R-tree, we devise a PR-tree index structure, in which each tree node stores the location and information of users geographical preferences. By traversing the PR-tree from the root in depth-first order, we can efficiently find the targeted users. Another challenge of the problem is to devise an algorithm for efficient seeds selection. To solve this challenge, we adopt the maximum influence arborescence (MIA) model to approximate the influence spread, and propose an efficient community-based seeds selection (CSS) algorithm. The proposed CSS algorithm finds seeds efficiently by constructing the PR-tree based indexes offline which precompute users community based influences, and preferentially computing the marginal influences of those who would be selected as seeds with high probability online. In particular, we propose a community detection algorithm which first computes the social influence based similarities by the MIA model and then adopts the spectral clustering algorithm to find optimal communities of the social network. Experimental results on real-world datasets collected from DoubanEvent demonstrate our proposed algorithm has superiority as compared to several state-of-the-art algorithms in terms of efficiency, while keeping large influence spread.