复杂网络中潜在感染状态的分类
Classifying Latent Infection States in Complex Networks
摘要：
识别网络中节点感染状态的算法对于理解和遏制感染至关重要。然而，通常只有相对较小的一组节点具有已知的感染状态。此外，每个节点被感染的时间长度也是未知的。这种缺失的数据-大多数节点的感染状态和未观察到的感染节点的感染时间-对真实世界级联的研究提出了挑战。在这项工作中，我们开发的技术，以确定潜在的感染节点中存在丢失的感染时间和状态数据。基于简单的可感染流行病模型预测的可能的流行病路径，我们提出了一个度量（感染介数）来揭示这些未知的感染状态。我们使用机器学习算法的实验结果表明，感染介数是识别潜在感染节点的最有效特征。
英文摘要：
Algorithms for identifying the infection states of nodes in a network are crucial for understanding and containing infections. Often, however, only a relatively small set of nodes have a known infection state. Moreover, the length of time that each node has been infected is also unknown. This missing data -- infection state of most nodes and infection time of the unobserved infected nodes -- poses a challenge to the study of real-world cascades. In this work, we develop techniques to identify the latent infected nodes in the presence of missing infection time-and-state data. Based on the likely epidemic paths predicted by the simple susceptible-infected epidemic model, we propose a measure (Infection Betweenness) for uncovering these unknown infection states. Our experimental results using machine learning algorithms show that Infection Betweenness is the most effective feature for identifying latent infected nodes.
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