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AuthorZhu, Wenlong
AuthorYang, Wu
AuthorXuan, Shichang
AuthorMan, Dapeng
AuthorWang, Wei
AuthorDu, Xiaojiang
AuthorGuizani, Mohsen
Available date2022-11-10T09:47:21Z
Publication Date2019
Publication NameIEEE Access
ResourceScopus
Resource2-s2.0-85062982497
URIhttp://dx.doi.org/10.1109/ACCESS.2019.2900708
URIhttp://hdl.handle.net/10576/36123
AbstractInfluence 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. 2013 IEEE.
SponsorThis work was supported in part by the National Natural Science Foundation of China under Grant 61572459 and Grant 61672180, in part by the Basic Scientific Research Project of Heilongjiang Education Department under Grant 135309469, and in part by the Teaching and Scientific Research Project of Qiqihar University under Grant 2016086 and Grant 201803.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectcompetitive social networks
Influence blocking maximization
location-based
TitleLocation-Based Seeds Selection for Influence Blocking Maximization in Social Networks
TypeArticle
Pagination27272-27287
Volume Number7
dc.accessType Open Access


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