Paper Info
Title
Location-Based Seeds Selection for Influence Blocking Maximization in Social Networks.
Abstract
Influence blocking maximization (IBM) is a key problem for viral marketing in competitive social networks. Although the IBM problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-based seeds selection for IBM problem, which aims to find a positive seed set in a given query region to block the negative influence propagation in a given block region as much as possible. In order to overcome the low efficiency of the simulation-based greedy algorithm, we propose a heuristic algorithm IS-LSS and its improved version IS-LSS+, both of which are based on the maximum influence arborescence structure and Quadtree index, while IS-LSS+ further improves the efficiency of IS-LSS by using an upper bound method and Quadtree cell lists. The experimental results on real-world datasets demonstrate that our proposed algorithms are able to achieve matching blocking effect to the greedy algorithm as the increase in the number of positive seeds and often better than other heuristic algorithms, whereas they are four orders of magnitude faster than the greedy algorithm.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2900708
IEEE ACCESS
Keywords
Field
DocType
Influence blocking maximization,location-based,competitive social networks
Mathematical optimization,Heuristic,Upper and lower bounds,Computer science,Heuristic (computer science),Cell lists,Greedy algorithm,Arborescence,Maximization,Quadtree,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Wenlong Zhu110.35
Yang Wu26922.62
Shichang Xuan374.14
Dapeng Man42910.54
Wei Wang510.69
X. Du62320241.73
Mohsen Guizani76456557.44