A Nearly-Linear Time Algorithm for Minimizing Risk of Conflict in Social Networks

Concomitant with the tremendous prevalence of online social media platforms, the interactions among individuals are unprecedentedly enhanced. People are free to interact with acquaintances, express and exchange their own opinions through commenting, liking, retweeting on online social media, leading...

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Vydáno v:arXiv.org
Hlavní autoři: Zhu, Liwang, Zhang, Zhongzhi
Médium: Paper
Jazyk:angličtina
Vydáno: Ithaca Cornell University Library, arXiv.org 13.01.2023
Témata:
Algorithms
Approximation
Combinatorial analysis
Digital media
Greedy algorithms
Mathematical analysis
Nodes
Optimization
Social networks
ISSN:2331-8422
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Shrnutí:Concomitant with the tremendous prevalence of online social media platforms, the interactions among individuals are unprecedentedly enhanced. People are free to interact with acquaintances, express and exchange their own opinions through commenting, liking, retweeting on online social media, leading to resistance, controversy and other important phenomena over controversial social issues, which have been the subject of many recent works. In this paper, we study the problem of minimizing risk of conflict in social networks by modifying the initial opinions of a small number of nodes. We show that the objective function of the combinatorial optimization problem is monotone and supermodular. We then propose a na\"ıve greedy algorithm with a \((1-1/e)\) approximation ratio that solves the problem in cubic time. To overcome the computation challenge for large networks, we further integrate several effective approximation strategies to provide a nearly linear time algorithm with a \((1-1/e-\epsilon)\) approximation ratio for any error parameter \(\epsilon>0\). Extensive experiments on various real-world datasets demonstrate both the efficiency and effectiveness of our algorithms. In particular, the fast one scales to large networks with more than two million nodes, and achieves up to \(20\times\) speed-up over the state-of-the-art algorithm.
Bibliografie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2301.05466