通过使用网络透的早期感染节点量化连锁疫情爆发的潜力
Xin Li1, Huichun Li1, Xue Zhang1
1College of Science, National University of Defense Technology, Changsha, Hunan 410073, China.
Chaos (Woodbury, N.Y.)
|April 17, 2024
概括
准确预测连锁疫情的爆发是至关重要的. 这项研究引入了一种使用网络结构和传播动态的新方法,用于早期和精确的级联爆发预测.
科学领域:
- 网络科学 网络科学
- 信息传播 信息传播
- 计算社会科学 计算社会科学
背景情况:
- 准确预测连锁疫情在各个领域至关重要.
- 了解信息传播动态是减轻负面影响的关键.
研究的目的:
- 开发一种用于估计信息传播概率的新型算法.
- 引入一种对级疫情的早期预测方法.
- 量化预测连锁疫情爆发的可能性和范围.
主要方法:
- 利用透理论提出一个全球传播概率算法.
- 整合网络结构数据和传播动态进行预测.
- 在各种网络结构和真实的微博数据集上进行实验.
主要成果:
- 级联疫情表现出类似相位过渡的行为.
- 特定的激活节点在疫情临界点附近至关重要.
- 拟议的方法展示了卓越的精度和早期检测能力.
结论:
- 开发的方法提供了对连锁疫情的强大和早期预测.
- 这种方法增强了对网络中信息传播的理解.
- 有效的预测有助于管理和控制级联现象.
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