Investigating the Resilience of Unstructured Supernode Networks

In this letter, we present a novel analytical framework to analyze the resilience of Unstructured Supernode Networks (USNs), where a "leaf" node can be promoted, after a fixed time interval, to the role of "supernode," with non-preferential attachment to a given number of existing supernodes. In particular, relying on an Absorbing Markov Chain (AMC)-based model of a supernode behavior, we derive an efficient approximation of the node degree distribution of an USN. This model also allows to estimate a supernode's probability of isolation. The proposed analytical framework is validated by simulation results.