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题名Community-based rumor blocking maximization in social networks: Algorithms and analysis
作者
发表日期2020-11-06
发表期刊Theoretical Computer Science
ISSN/eISSN0304-3975
卷号840页码:257-269
摘要

Social networks provide us a convenient platform to communicate and share information or ideas with each other, but it also causes many negative effects at the same time, such as, the spread of misinformation or rumor in social networks may cause public panic and even serious economic or political crisis. In this paper, we propose a Community-based Rumor Blocking Problem (CRBMP), i.e., selecting a set of seed users from all communities as protectors with the constraint of budget b such that the expected number of users eventually not being influenced by rumor sources is maximized. We consider the community structure in social networks and solve our problem in two stages, in the first stage, we allocate budget b for all the communities, this sub-problem whose objective function is proved to be monotone and DR-submodular, so we can use the method of submodular function maximization on an integer lattice, which is different from most of the existing work with the submodular function over a set function. Then a greedy community budget allocation algorithm is devised to get an 1−1/e approximation ratio; we also propose a speed-up greedy algorithm which greatly reduces the time complexity for the community budget allocation and can get an 1−1/e−ϵ approximation guarantee meanwhile. Next we solve the Protector Seed Selection (PSS) problem in the second stage after we obtained the budget allocation vector for communities, we greedily choose protectors for each community with the budget constraints to achieve the maximization of the influence of protectors. The greedy algorithm for PSS problem can achieve a 1/2 approximation guarantee. We also consider a special case where the rumor just originates from one community and does not spread out of its own community before the protectors are selected, the proposed algorithm can reduce the computational cost than the general greedy algorithm since we remove the uninfected communities. Finally, we conduct extensive experiments on three real world data sets, the results demonstrate the effectiveness of the proposed algorithm and its superiority over other methods.

关键词Community structure Influence maximization Rumor blocking Social network
DOI10.1016/j.tcs.2020.08.030
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收录类别SCIE
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Theory & Methods
WOS记录号WOS:000572872700017
Scopus入藏号2-s2.0-85091219333
引用统计
被引频次:23[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/9158
专题个人在本单位外知识产出
通讯作者Huang, Chuanhe
作者单位
1.School of Computer Science,Wuhan University,China
2.Collaborative Innovation Center of Geospatial Technology,Wuhan,China
3.Department of Computer Science,The University of Texas at Dallas,Richardson,United States
推荐引用方式
GB/T 7714
Ni, Qiufen,Guo, Jianxiong,Huang, Chuanheet al. Community-based rumor blocking maximization in social networks: Algorithms and analysis[J]. Theoretical Computer Science, 2020, 840: 257-269.
APA Ni, Qiufen, Guo, Jianxiong, Huang, Chuanhe, & Wu, Weili. (2020). Community-based rumor blocking maximization in social networks: Algorithms and analysis. Theoretical Computer Science, 840, 257-269.
MLA Ni, Qiufen,et al."Community-based rumor blocking maximization in social networks: Algorithms and analysis". Theoretical Computer Science 840(2020): 257-269.
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