# Network Disruption via Continuous Batch Removal: The Case of Sicilian Mafia

arXiv: https://arxiv.org/abs/2310.09987 · HTML: https://mlsanity.com/paper/2310.09987/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2023.

## Built on by (most cited first, 2 of 2)

- [Efficient Disruption of Criminal Networks through Multi-Objective Genetic Algorithms](https://mlsanity.com/paper/2604.09647/) (2026)
- [Semantic Networks as Clues: A Theoretical Foundation and Process Optimization for Semantic Network Construction](https://mlsanity.com/paper/2608.01936/) (2026)

## Abstract (arXiv)

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.