一种双相特征选择方法,用于在复杂网络中识别疾病流行病的有影响力的传播者.
Xiya Wang1, Yuexing Han1,2, Bing Wang1
1School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
在流行病中识别有影响力的传播者至关重要. 这项研究引入了一种两相特征选择方法,以有效地确定这些关键个体,改进疫情控制策略.
科学领域:
- 网络流行病学 网络流行病学
- 计算社会科学 计算社会科学
- 传染病建模传染病建模
背景情况:
- 识别有影响力的传播者对于了解流行病动态和控制至关重要.
- 对于大型网络而言,传统的中心性衡量方法在普遍性和计算效率方面存在局限性.
- 机器学习方法可以改善扩散器识别,但可能是计算密集的.
研究的目的:
- 开发一种计算效率高,双相特征选择方法,用于识别有影响力的传播者.
- 确定最佳的特征组合,在不同类型的网络中进行扩散器识别.
- 为了减少特征维度,同时保持确定关键个体的准确性.
主要方法:
- 采用了两阶段的特征选择方法.
- 根据网络拓 (Barabasi-Albert,Erdős-Rényi,Watts-Strogatz) 和数据集不平衡,确定了最佳特征组合.
- 该方法在合成和现实世界的网络数据上得到了验证.
主要成果:
- 对于巴巴巴西-阿尔伯特 (BA) 网络来说,包括双跳邻里中心性的组合是基本的.
- 对于Erdős-Rényi (ER) 网络来说,高度的中心性是必不可少的.
- 发现Watt-Strogatz (WS) 网络不需要选择特征,现实世界网络中选择的特征与合成网络的发现具有很高的相似性.
结论:
- 拟议的两阶段特征选择方法有效地减少了用于识别有影响力的传播者的特征尺寸.
- 最佳的功能组合是网络依赖的,为不同的网络结构提供量身定制的策略.
- 这种方法为识别超级传播者提供了一种新且有效的途径,以加强疫情控制.
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