在fodemic源检测与信息流:基础和可扩展计算
Zimeng Wang1, Chao Zhao1, Qiaoqiao Zhou2
1Department of Computer Science, City University of Hong Kong, Hong Kong, China.
Entropy (Basel, Switzerland)
|September 27, 2025
概括
这项研究引入了一种用于谣言来源检测的新型通用估计器,改进了在复杂网络中失败的传统方法. 新方法提高了在网络传播过程中识别信息来源的准确性和可扩展性.
科学领域:
- 网络科学 网络科学
- 信息理论 信息理论
- 计算社会科学 计算社会科学
背景情况:
- 传统的谣言来源识别方法,如使用易感染性 (SI) 模型的最大概率 (ML) 和联合最大概率 (JML) 估计器,遭受了退化.
- 这些经典方法往往无法独特地识别谣言来源,即使是在基本的网络配置中.
研究的目的:
- 开发一种更强大,更准确的方法来识别网络中谣言的来源.
- 通过结合随机观察时间和先进的网络流概念来克服现有估计器的局限性.
主要方法:
- 提出了一个包含独立随机观察时间的通用估计器.
- 形成的信息流超越了简单的图表,考虑周期性多联网网络的速率限制和多播能力.
- 开发了前置消除和后向搜索算法,用于速度受约束的源检测.
主要成果:
- 与经典方法相比,通用估计器在识别谣言来源方面表现得更好.
- 模拟验证了为速度受约束源检测提出的算法的有效性和可扩展性.
- 该研究为复杂网络环境中的信息流行源检测提供了严格的基础.
结论:
- 新的通用估计器在谣言来源检测方面取得了重大进展,特别是在具有挑战性的网络结构中.
- 开发的算法是有效和可扩展的,为分析信息传播提供了实用工具.
- 这项研究为了解和减轻信息流行病的影响建立了一个强大的框架.
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