开发一个基于代理的模型，以最大限度地减少恶意信息在动态社交网络中的传播。

IF 1.8 4区管理学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computational and Mathematical Organization Theory Pub Date : 2023-04-12 DOI:10.1007/s10588-023-09375-6

Mustafa Alassad, Muhammad Nihal Hussain, Nitin Agarwal

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引用次数: 1

摘要

本研究引入了一个系统的、多学科的基于agent的模型来解释和简化在线(离线)社交网络中用户和社区的动态行为。采用组织控制论的方法对恶意信息在社区间的传播进行控制和监控。随机一中值问题最大限度地减少了代理响应时间，并消除了在线(离线)环境中传播的信息。这些方法的效果是在与2020年5月在密歇根州举行的反对新冠肺炎封锁的武装抗议示威相关的推特网络中进行的。该模型展示了网络的动态特性，提高了智能体级性能，最小化了恶意信息的传播，并测量了网络对第二次随机信息传播的响应。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Developing an agent-based model to minimize spreading of malicious information in dynamic social networks.

This research introduces a systematic and multidisciplinary agent-based model to interpret and simplify the dynamic actions of the users and communities in an evolutionary online (offline) social network. The organizational cybernetics approach is used to control/monitor the malicious information spread between communities. The stochastic one-median problem minimizes the agent response time and eliminates the information spread across the online (offline) environment. The performance of these methods was measured against a Twitter network related to an armed protest demonstration against the COVID-19 lockdown in Michigan state in May 2020. The proposed model demonstrated the dynamicity of the network, enhanced the agent level performance, minimized the malicious information spread, and measured the response to the second stochastic information spread in the network.

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来源期刊

Computational and Mathematical Organization Theory COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.80

自引率

16.70%

发文量

审稿时长

>12 weeks

期刊介绍： Computational and Mathematical Organization Theory provides an international forum for interdisciplinary research that combines computation, organizations and society. The goal is to advance the state of science in formal reasoning, analysis, and system building drawing on and encouraging advances in areas at the confluence of social networks, artificial intelligence, complexity, machine learning, sociology, business, political science, economics, and operations research. The papers in this journal will lead to the development of newtheories that explain and predict the behaviour of complex adaptive systems, new computational models and technologies that are responsible to society, business, policy, and law, new methods for integrating data, computational models, analysis and visualization techniques. Various types of papers and underlying research are welcome. Papers presenting, validating, or applying models and/or computational techniques, new algorithms, dynamic metrics for networks and complex systems and papers comparing, contrasting and docking computational models are strongly encouraged. Both applied and theoretical work is strongly encouraged. The editors encourage theoretical research on fundamental principles of social behaviour such as coordination, cooperation, evolution, and destabilization. The editors encourage applied research representing actual organizational or policy problems that can be addressed using computational tools. Work related to fundamental concepts, corporate, military or intelligence issues are welcome.