2014/01/31 by Yeon-sup Lim, Lim, Yeon-sup, Bruno Ribeiro +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · Psychology · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Mental Health Research Topics #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1402.0013
arxiv created 2014/01/31 · openalex publication_date 2014/01/31 · arxiv updated 2014/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.