从杂和不完整的数据中学习网络传播模型
Mateusz Wilinski1,2, Andrey Y Lokhov1
1Theoretical Division, <a href="https://ror.org/01e41cf67">Los Alamos National Laboratory</a>, Los Alamos, New Mexico 87545, USA.
Physical review. E
|December 18, 2024
概括
这项研究提出了网络传播动态的通用学习方法,解决了未知结构和杂数据等挑战. 可扩展的动态消息传递技术高效地重建网络和参数,即使信息有限.
科学领域:
- 网络科学 网络科学
- 计算流行病学计算流行病学
- 机器学习是机器学习.
背景情况:
- 学习传播动态的参数至关重要,但面临诸如未知的网络结构,噪音数据和缺失的观测等挑战.
- 准确的学习通常需要通过有效地整合先前信息来最小化样本大小.
研究的目的:
- 引入一种普遍的学习方法来传播动态,解决现实世界数据中常见的挑战.
- 使用可用的先前知识,重建网络结构和扩散模型的参数.
主要方法:
- 作为通用学习方法的核心,采用了可扩展的动态信息传递技术.
- 该算法整合了关于模型和数据的先前知识,以克服学习障碍.
主要成果:
- 拟议的方法有效地解决了诸如未知的网络结构,杂的数据和缺失的观测等挑战.
- 它成功地重建了网络拓和扩散模型参数.
- 该方法在关键模型参数方面表现出线性计算复杂性,确保可扩展性.
结论:
- 开发的通用学习方法为扩散动态中的参数学习提供了可扩展和高效的解决方案.
- 它证明了处理复杂的现实世界数据场景的能力,包括不完整的信息和未知的网络结构.
- 该技术的可扩展性使其适合分析大规模网络实例.
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