Efficient Distance-Aware Aware Influence Maximization in Geo-Social Geo Networks
Abstract: Given a social network and a positive integer k, the influence maximization problem aims to identify a set of k nodes in that can maximize the influence spread under a certain propagation model. As the proliferation of geo-social geo networks, location-aware aware p promotion romotion is becoming more necessary in real applications. In this paper, we study the distance distance-aware aware influence maximization (DAIM) problem, which advocates the importance of the distance between users and the promoted location. Unlike the traditional influ influence ence maximization problem, DAIM treats users differently based on their distances from the promoted location. In this situation, the k nodes selected are different when the promoted location varies. In order to handle the large number of queries and meet the he online requirement, we develop two novel index index-based based approaches, MIA-DA and RIS-DA, DA, by utilizing the information over some pre pre-sampled sampled query locations. MIA-DA DA is a heuristic method which adopts the maximum influence arborescence (MIA) model to approxima approximate te the influence calculation. In addition, different pruning strategies as well as a priority priority-based based algorithm are proposed to significantly reduce the searching space. To improve the effectiveness, in RIS-DA, RIS we extend the reverse influence sampling (RIS) model and come up with an unbiased estimator for the DAIM problem. Through carefully analyzing the sample size needed for indexing, RIS RIS-DA is able to return a 1 - 1=e - Îľ approximate