  Network dismantling is a relevant research area in network science, gathering
attention both from a theoretical and an operational point of view. Here, we
propose a general framework for dismantling that prioritizes the removal of
nodes that bridge together different network communities. The strategies we
detect are not unique, as they depend on the specific realization of the
community detection algorithm considered. However, when applying the
methodology to some real-world networks we find that the percolation threshold
at which dismantling occurs is strongly robust, and it does not depend on the
specific algorithm. Thus, the stochasticity inherently present in many
community detection algorithms allows to identify several strategies that have
comparable effectiveness but require the removal of distinct subsets of nodes.
This feature is highly relevant in operational contexts in which the removal of
nodes is costly and allows to identify the least expensive strategy that still
holds high effectiveness.
