  Although community structure is ubiquitous in complex networks, few works
exploit this topological property to control epidemics. In this work, devoted
to networks with non-overlapping community structure (i.e, a node belongs to a
single community), we propose and investigate three deterministic immunization
strategies. In order to characterize the influence of a node, various pieces of
information are used such as the number of communities that the node can reach
in one hop, the nature of the links (intra community links, inter community
links), the size of the communities, and the interconnection density between
communities. Numerical simulations with the Susceptible-Infected-Removed (SIR)
epidemiological model are conducted on both real-world and synthetic networks.
Experimental results show that the proposed strategies are more effective than
classical deterministic alternatives that are agnostic of the community
structure. Additionally, they outperform stochastic and deterministic
strategies designed for modular networks.
