Multi-Community Influence Maximization in Device-to-Device social networks

In recent years, we have witnessed the rapid development of mobile multimedia services integrated with social networks. Therefore, Influence Maximization (IM) problem in social networks has become a widely studied topic, which aims to identify a small set of users (seed users) to cover as many users as possible through information propagation. Although most researches focus on online occasions or one single community, a few studies have been done for face-to-face (Device-to-Device, D2D) propagation occasions across multiple communities. General influence maximization in one community aims to find out seed users under the given budget , while in this paper, we concentrate on Multi-Community Influence Maximization (MCIM) problem to maximize influence (i.e., propagation coverage) by identifying seed users in multiple social communities of different properties and characteristics based on a total budget of seed users. We transform this problem into two subproblems, including Single Community Influence Maximization (SCIM) and Multi-Community Budget Allocation (MCBA). Respectively, we propose Weighted LeaderRank with Neighbors (WLRN) to rank users in a single community and design a method named Optimal Budget Allocation (OBA) to allocate budget (total quota of seed users) to multiple communities. The experiments based on a realistic D2D data set and an online social network show our method improves the propagation coverage significantly than general algorithms.