Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings

Research Article

An Efficient Critical Incident Propagation Model for Social Networks Based on Trust Factor

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  • @INPROCEEDINGS{10.1007/978-3-030-00916-8_39,
        author={XiaoMing Li and Limengzi Yuan and ChaoChao Liu and Wei Yu and Xue Chen and Guangquan Xu},
        title={An Efficient Critical Incident Propagation Model for Social Networks Based on Trust Factor},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2018},
        month={10},
        keywords={Trust factor Cps Social networks Incident propagation},
        doi={10.1007/978-3-030-00916-8_39}
    }
    
  • XiaoMing Li
    Limengzi Yuan
    ChaoChao Liu
    Wei Yu
    Xue Chen
    Guangquan Xu
    Year: 2018
    An Efficient Critical Incident Propagation Model for Social Networks Based on Trust Factor
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-00916-8_39
XiaoMing Li,*, Limengzi Yuan1,*, ChaoChao Liu1,*, Wei Yu1,*, Xue Chen1,*, Guangquan Xu1,*
  • 1: Tianjin University
*Contact email: lxm696@163.com, ylmz@tju.edu.cn, chaochaoliu@tju.edu.cn, weiyu@tju.edu.cn, xuechen@tju.edu.cn, losin@tju.edu.cn

Abstract

Studying patterns of social behavior among users based on micro blogs, QQ posts, and comments is essential to understanding the information propagation process during critical incidents. A common problem of information propagation models based on epidemic dynamics is that they regard the probability of information being propagated successfully across different nodes as a constant. But in real-world scenarios, infection probability varies depending on the trust relationship between people. In this paper, a novel information propagation model for critical incidents is proposed that takes into account the trust factor based on information propagation theory.