A GCN/GAT framework predicts IPR values of the principal eigenvector and classifies networks into delocalized, weakly localized, and strongly localized states with roughly 95 percent accuracy on synthetic test networks.
Multilevel Digital Contact Tracing
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abstract
Digital contact tracing plays a crucial role in alleviating an outbreak, and designing multilevel digital contact tracing for a country is an open problem due to the analysis of large volumes of temporal contact data. We develop a multilevel digital contact tracing framework that constructs dynamic contact graphs from the proximity contact data. Prominently, we introduce the edge label of the contact graph as a binary circular contact queue, which holds the temporal social interactions during the incubation period. After that, our algorithm prepares the direct and indirect (multilevel) contact list for a given set of infected persons from the contact graph. Finally, the algorithm constructs the infection pathways for the trace list. We implement the framework and validate the contact tracing process with synthetic and real-world data sets. In addition, analysis reveals that for COVID-19 close contact parameters, the framework takes reasonable space and time to create the infection pathways. Our framework can apply to any epidemic spreading by changing the algorithm's parameters.
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cs.LG 1years
2025 1verdicts
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Predicting Steady-State Behavior in Complex Networks with Graph Neural Networks
A GCN/GAT framework predicts IPR values of the principal eigenvector and classifies networks into delocalized, weakly localized, and strongly localized states with roughly 95 percent accuracy on synthetic test networks.