开发基于代理的模型,以尽量减少恶意信息在动态社交网络中的传播
Mustafa Alassad1, Muhammad Nihal Hussain2, Nitin Agarwal1
1COSMOS Research Center, UA-Little Rock, Little Rock, AR USA.
概括
本研究提出了一个基于代理的模型来管理在线社交网络和制错误信息. 该模型有效地减少了恶意信息的传播,并在动态网络环境中提高了代理的性能.
科学领域:
- 计算社会科学 计算社会科学
- 网络科学 网络科学
- 网络学就是网络学.
背景情况:
- 在线社交网络表现出复杂的动态,受用户和社区互动的影响.
- 恶意信息的传播在这些不断发展的网络中构成了重大挑战.
- 控制信息传播需要复杂的建模和监控方法.
研究的目的:
- 引入一个系统的,多学科的基于代理的模型来分析在线社交网络的动态.
- 应用组织网络学来监控和控制恶意信息的传播.
- 为了优化代理响应时间并减轻信息传播,使用随机一个中位数问题.
主要方法:
- 开发一个系统的基于代理的模型,整合组织网络学.
- 应用随机一个中位数问题的应用,以最大限度地减少代理反应时间.
- 使用密歇根州COVID-19封锁抗议活动的Twitter网络数据集进行实证验证.
主要成果:
- 该模型成功地捕捉到了社交网络的动态性.
- 增强了代理级别的性能,并大大减少了恶意信息的传播.
- 量化了网络对第二波随机信息传播的反应.
结论:
- 拟议的基于代理的模型为理解和管理在线社交网络提供了有效的框架.
- 组织网络学和随机优化是打击错误信息的宝贵工具.
- 该模型在现实世界的网络场景中展示了强大的性能,为信息控制策略提供了洞察力.
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