Paper Info
Title
Multi-Community Influence Maximization in Device-to-Device social networks
Abstract
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 k seed users under the given budget k, 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.
Year
DOI
Venue
2021
10.1016/j.knosys.2021.106944
Knowledge-Based Systems
Keywords
DocType
Volume
Influence maximization,Multiple communities,Budget allocation,Device-to-Device (D2D),Social networks
Journal
221
ISSN
Citations 
PageRank 
0950-7051
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Xiaofei Wang168658.88
Xu Tong200.68
Hao Fan300.68
Chenyang Wang4816.10
Jianxin Li544348.67
Xin Wang66524.90