在基于超图的社交网络中传播信息
Hai-Bing Xiao1,2, Feng Hu1,2, Peng-Yue Li1,2
1School of Computer, Qinghai Normal University, Xining 810008, China.
Entropy (Basel, Switzerland)
|November 27, 2024
概括
这项研究引入了一个新的超级网络信息传播模型 (SSEIR) 用于社交网络. 它准确地模拟复杂的交互,并提高对在线信息传播的理解.
科学领域:
- 计算社会科学 计算社会科学
- 网络科学 网络科学
- 信息理论 信息理论
背景情况:
- 由于复杂的用户互动,社交网络对信息传播建模提出了独特的挑战.
- 像SEIR这样的传统模型可能无法完全捕捉在线平台固有的高阶,多用户动态.
研究的目的:
- 为在线社交网络开发一种新的超级网络信息传播模型 (SSEIR).
- 通过纳入超图理论和完善状态过渡来增强传统的SEIR模型.
- 准确模拟和分析在复杂的在线社会结构中的信息传播.
主要方法:
- 使用超图理论增强了传统的SEIR模型,以创建SSEIR模型.
- 将敏感状态转化为活性 (Sa) 和非活性 (Si) 状态.
- 纳入反应过程 (RP) 策略和衍生微分动态方程.
- 在各种网络结构和真实数据集上利用平均场理论进行理论推导和模拟实验.
主要成果:
- 该SSEIR模型有效模拟在线社交网络中的信息传播和传播.
- 与传统模型相比,展示了优越的数据拟合能力.
- 成功地说明了信息在超级网络结构中传播的内部机制和动态进化模式.
- 通过对真实世界网络数据的应用来验证模型的实用性.
结论:
- 该SSEIR模型提供了一个更准确的信息在复杂的在线社交网络传播的表现.
- 这项研究丰富了信息传播的理论框架.
- 为新闻传播,论管理和谣言监测等实际应用提供科学基础.
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