  Network disruption is pivotal in understanding the robustness and
vulnerability of complex networks, which is instrumental in devising strategies
for infrastructure protection, epidemic control, cybersecurity, and combating
crime. In this paper, with a particular focus on disrupting criminal networks,
we proposed to impose a within-the-largest-connected-component constraint in a
continuous batch removal disruption process. Through a series of experiments on
a recently released Sicilian Mafia network, we revealed that the constraint
would enhance degree-based methods while weakening betweenness-based
approaches. Moreover, based on the findings from the experiments using various
disruption strategies, we propose a structurally-filtered greedy disruption
strategy that integrates the effectiveness of greedy-like methods with the
efficiency of structural-metric-based approaches. The proposed strategy
significantly outperforms the longstanding state-of-the-art method of
betweenness centrality while maintaining the same time complexity.
