Identifying important nodes to affect diffusion on networks is a key question in network theory that pertains to a wide range of fields and is not limited to infectious disease dynamics only. There are however two major issues associated with this problem: (i) the structure of networks is often not known, and (ii) many networks are too large to compute, for example, centrality measures efficiently. Stochastic algorithms like the proposed CBF algorithm or the acquaintance method address both problems at once. To what extent targeted immunization strategies can be implemented in a infectious diseases/public health setting based on practical and ethical considerations remains an open question. This is true not only for the strategy based on the CBF algorithm, but for most strategies that are based on network properties. As mentioned above, the contact networks relevant for the spread of infectious diseases are generally not known. Stochastic algorithms such as the CBF or the acquaintance method are at least in principle applicable when data on network structure is lacking.