Dynamic Centrality in Metapopulation Networks: Incorporating Dynamics and Network Structure

In epidemic networks, walk-based centrality indices are often used to identify the nodes that are significantly contributing to the spread of disease. While the network topology can provide a good insight into how the disease might propagate throughout the network, epidemic-related factors can change the ranking results as well. This paper presents a dynamics-based node centrality that incorporates epidemic characteristics, internal time delays, and network structure at the same time. This centrality allows for dynamic identification of the nodes that are more sensitive to external shocks, which in turn can help prevent performance degradation in the network. It is shown that some of the prominent walk-based centralities, such as local and eigenvector centralities, are in fact correlated with dynamics-based centrality for certain epidemic parameters