| 研究実績の概要 |
In FY 2023, we primarily investigated the relevance of network structures extracted from data in spreading dynamics. We focused on how these structures could be used to design efficient containment strategies. The project examined the role of backbones and temporal cores in spreading processes and found that acting on these structures can help contain epidemic outbreaks. Our project also explored the relative timescales of observation, network evolution, and the dynamic process under study. By identifying the optimal time window for aggregating time-varying networks, we were able to reduce redundancy and simplify the modeling of spreading processes on temporal networks by embedding techniques. We, furthermore, investigated the impact of incompleteness or noise on the outcome of data-driven models using previously developed representations and developed approaches to compensate for resulting biases.
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