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<p>Efficiency of a system network often relies on high connectivity. However, strongly connected networks are vulnerable in a case of a spreading virus. In our study, we propose a clustering method which balances the two opposing factors: maintains a high system efficiency yet minimises the spreading potential. Our Deep Epidemic Efficiency Network (DEEN) model leverages Graph Convolutional Neural Networks and a novel loss function. In an unsupervised setting, we seek a partition that maximises the system utility while restraining the transmission rate to a desired level. We show that proposed method successfully solves three real-life problems: ride-pooling service in New York City, economic exchange between regions in Poland, and information sharing via peer-to-peer network. In particular, by dividing 150 New York taxi travellers into four groups our method increases epidemic threshold more than twofold at the cost of reducing utility only by <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(13\%\)</EquationSource> </InlineEquation>. The model can be instrumental in future pandemic outbreaks when we need to balance between efficiency and potential spread of a virus.</p>

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Optimising network efficiency in an epidemic scenario

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月11日

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  • Magdalena Proszewska,
  • Michal Bujak,
  • Rafal Kucharski,
  • Jacek Tabor,
  • Marek Smieja

摘要

Efficiency of a system network often relies on high connectivity. However, strongly connected networks are vulnerable in a case of a spreading virus. In our study, we propose a clustering method which balances the two opposing factors: maintains a high system efficiency yet minimises the spreading potential. Our Deep Epidemic Efficiency Network (DEEN) model leverages Graph Convolutional Neural Networks and a novel loss function. In an unsupervised setting, we seek a partition that maximises the system utility while restraining the transmission rate to a desired level. We show that proposed method successfully solves three real-life problems: ride-pooling service in New York City, economic exchange between regions in Poland, and information sharing via peer-to-peer network. In particular, by dividing 150 New York taxi travellers into four groups our method increases epidemic threshold more than twofold at the cost of reducing utility only by \(13\%\) . The model can be instrumental in future pandemic outbreaks when we need to balance between efficiency and potential spread of a virus.