Precautionary rumor containment via trustworthy people in social networks

被引:5
|
作者
Fan, Lidan [1 ]
Wu, Weili [2 ]
Xing, Kai [2 ]
Lee, Wonjun [3 ]
机构
[1] Univ Kansas, Sch Business, 1450 Jayhawk Blvd, Lawrence, KS 66045 USA
[2] Univ Texas Dallas, Dept Comp Sci, 800 W Campbell Rd, Richardson, TX 75080 USA
[3] Korea Univ, Dept Comp Sci & Engn, 145 Anam Ro, Seoul, South Korea
基金
美国国家科学基金会;
关键词
Rumor; trust; social networks; social relation graph; Greedy Algorithm;
D O I
10.1142/S179383091650004X
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In a social network, rumor containment is vital, as the diffusion of a rumor will bring terrible results. Precautionary measure can be used to control rumor propagation: Anticipating the spread of a rumor, one can (1) select a set of trustworthy people (TP) in the network, (2) alert the TP about the rumor, and (3) ask the TP to protect their neighbors by sending out alerts. In this paper, we study the problem of how to select the least number of TP, satisfying the requirement that the entire network is protected by the alerts that the TP send. We propose an asymmetric trust (AT) information propagation model. Under this model, we study the Least Number TP Selection (LNTS) problem, establish its NP-hardness and reformulate it as a minimum submodular cover problem. As a result, the Greedy Algorithm is a constant-factor approximation algorithm. Using real-world data, we evaluate the performance of the Greedy Algorithm, and compare it with other algorithms. Experimental results indicate that the Greedy Algorithm performs the best among its competitors.
引用
收藏
页数:18
相关论文
共 50 条
  • [41] A rumor transmission model with incubation in social networks
    Jia, Jianwen
    Wu, Wenjiang
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 491 : 453 - 462
  • [42] Least Cost Rumor Blocking in Social Networks
    Fan, Lidan
    Lu, Zaixin
    Wu, Weili
    Thuraisingham, Bhavani
    Ma, Huan
    Bi, Yuanjun
    2013 IEEE 33RD INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS), 2013, : 540 - 549
  • [43] Trustworthy situation assessment via belief networks
    Das, S
    Lawless, D
    PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, VOL I, 2002, : 543 - 549
  • [44] Trustworthy adaptive adversarial perturbations in social networks
    Zhang, Jiawei
    Wang, Jinwei
    Wang, Hao
    Luo, Xiangyang
    Ma, Bin
    JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, 2024, 80
  • [45] Blockchain solutions for trustworthy decentralization in social networks
    Mlika, Fatma
    Karoui, Wafa
    Ben Romdhane, Lotfi
    COMPUTER NETWORKS, 2024, 244
  • [46] Towards Trustworthy Participants in Social Participatory Networks
    Xu, Guangquan
    Liu, Bin
    Ren, Yuanyuan
    Huang, Runhe
    Zhang, Gaoxu
    Feng, Zhiyong
    Li, Xiaohong
    2015 IEEE 2ND INTERNATIONAL CONFERENCE ON CYBER SECURITY AND CLOUD COMPUTING (CSCLOUD), 2015, : 194 - 199
  • [47] Rumor detection on social media using hierarchically aggregated feature via graph neural networks
    Shouzhi Xu
    Xiaodi Liu
    Kai Ma
    Fangmin Dong
    Basheer Riskhan
    Shunzhi Xiang
    Changsong Bing
    Applied Intelligence, 2023, 53 : 3136 - 3149
  • [48] Rumor detection on social media using hierarchically aggregated feature via graph neural networks
    Xu, Shouzhi
    Liu, Xiaodi
    Ma, Kai
    Dong, Fangmin
    Riskhan, Basheer
    Xiang, Shunzhi
    Bing, Changsong
    APPLIED INTELLIGENCE, 2023, 53 (03) : 3136 - 3149
  • [49] On Misinformation Containment in Online Social Networks
    Tong, Guangmo
    Wu, Weili
    Du, Ding-Zhu
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
  • [50] A Stochastic Game Model for Analysis of Rumor and Anti-rumor Propagation in Social Networks
    Ding Qing
    Zang ZhengGong
    2020 IEEE 6TH INTERNATIONAL CONFERENCE ON CONTROL SCIENCE AND SYSTEMS ENGINEERING (ICCSSE), 2019, : 63 - 67