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Found 4 papers, 2 papers with code
Learning of networked spreading models from noisy and incomplete data
Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data.
Congruity of genomic and epidemiological data in modeling of local cholera outbreaks
Cholera continues to be a global health threat.
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
Spreading processes play an increasingly important role in modeling for diffusion networks, information propagation, marketing and opinion setting.
Network Sensitivity of Systemic Risk
In particular, we consider networks of varying density and with different block structures, and diversify as well in the details of the shock propagation dynamics.
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