发表状态 | 已发表Published |
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
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ISSN/eISSN | 0304-3975 |
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 |
DOI | 10.1016/j.tcs.2020.08.030 |
URL | 查看来源 |
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 | |
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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