Optimising Network Efficiency in the Epidemic Scenario
10 Pages Posted: 16 Oct 2024
Abstract
We consider the problem of reducing virus spreading in the system network (graph) while keeping the utility of the whole system at the maximal level. To balance the above two opposite goals, we propose Deep Epidemic Efficiency Network (DEEN), an unsupervised clustering method, which optimises graph efficiency in an epidemic scenario using Graph Convolutional Neural Networks and a novel loss function. Given the desired virus transmission, it constructs a graph partition for which the predefined transmission rate is not exceeded and utility function is maximised. 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 maintaining efficiency and preventing the spread of the virus.
Keywords: Graph neural networks, virus spread, Graph Clustering
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