  We investigate the use of a recently proposed centrality tuple called the
Neighborhood-based Bridge Node Centrality (NBNC) tuple to choose nodes for
preferential vaccination so that such vaccinated nodes could provide herd
immunity and reduce the spreading rate of infections in a complex real-world
network. The NBNC tuple ranks nodes on the basis of the extent they play the
role of bridge nodes in a network. A node is a bridge node, if when removed its
neighbors are either disconnected or at least sparsely connected. We
hypothesize that preferentially vaccinating such bridge nodes would block an
infection to spread from a neighbor of the bridge node to an another neighbor
that are otherwise not reachable to each other. We evaluate the effectiveness
of using NBNC to reduce the spread of infections by conducting simulations of
the spread of infections per the SIS (Susceptible-Infected-Susceptible) model
on a collection of 10 complex real-world social networks. We observe the
average fraction of infected nodes per round of the SIS simulations based on
NBNC for preferential vaccination to be lower than that of the degree
centrality-based preferential vaccination.
