Online conflict resolution: Algorithm design and analysis

被引:0
|
作者
Rao, Guoyao [1 ]
Li, Deying [2 ]
Wang, Yongcai [2 ]
Chen, Wenping [2 ]
Zhou, Chunlai [2 ]
Zhu, Yuqing [3 ]
机构
[1] Univ Elect Sci & Technol China, Yangtze Delta Reg Inst Quzhou, Quzhou 324000, Peoples R China
[2] Renmin Univ China, Sch Informat, Beijing 100872, Peoples R China
[3] Zhejiang Lab, Res Ctr Appl Math & Machine Intelligence, Hangzhou, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Online conflicts; Influence maximization; Social networks; INFLUENCE MAXIMIZATION;
D O I
10.1016/j.ins.2023.119718
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In today's digital age, the widespread dissemination of information through social media platforms has a profound impact on people's thoughts and actions. It fosters lively discussions and debates among individuals, but also sparks online conflicts that exacerbate social tensions and divisions. Therefore, it brings a challenge: how to eliminate these online conflicts. In this paper, we analyze and solve it from an algorithmic perspective. We introduce individuals who are willing to persuade their close friends to avoid conflicts with others. Our objective is to strategically select a group of influencers who can encourage others to act as mediators, thus mitigating online conflicts within the realm of social networks. Hence, an optimization problem is formulated, which is proved to be NP-hard and #P-hard. Subsequently, to solve the problem, an algorithm grounded in a reverse sampling technique is proposed, which achieves a performance bound of (1 - 1/������). Finally, the result of experiments on real-world networks datasets to evaluate our algorithm and shows that our algorithm outperforms all comparisons.
引用
收藏
页数:15
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