Understanding and improving the robustness of networks has significant applications in various areas, such as bioinformatics, transportation, critical infrastructures, and social networks. Recently, there has been a large amount of work on network dismantling, which focuses on removing an optimal set of nodes to break the network into small components with sub-extensive sizes. However, in our experiments, we found these state-of-the-art methods, although seemingly different, utilize the same refinement technique, namely reinsertion, to improve the performance. Despite being mentioned with understatement, the technique essentially plays the key role in the final performance. Without reinsertion, the current best method would deteriorate worse than the simplest heuristic ones; while with reinsertion, even the random removal strategy achieves on par with the best results. As a consequence, we, for the first time, systematically revisit the power of reinsertion in network dismantling problems. We re-implemented and compared 10 heuristic and approximate competing methods on both synthetic networks generated by four classical network models, and 18 real-world networks which cover seven different domains with varying scales. The comprehensive ablation results show that: i) HBA (High Betweenness Adaption, no reinsertion) is the most effective network dismantling strategy, however, it can only be applicable in small scale networks; ii) HDA (High Degree Adaption, with reinsertion) achieves the best balance between effectiveness and efficiency; iii) The reinsertion techniques help improve the performance for most current methods; iv) The one, which adds back the node based on that it joins the clusters minimizing the multiply of both numbers and sizes, is the most effective reinsertion strategy for most methods. Our results can be a survey reference to help further understand the current methods and thereafter design the better ones.

中文翻译：

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重新探寻最佳网络攻击目标的重新插入功能

了解并提高网络的健壮性在生物信息学，交通运输，关键基础设施和社交网络等各个领域都有重要的应用。最近，在网络拆解方面进行了大量工作，其工作重点是删除最佳节点集，以将网络分解为规模较小的小型组件。但是，在我们的实验中，我们发现这些最先进的方法尽管看似有所不同，但它们利用相同的优化技术（即重新插入）来提高性能。尽管提到的内容有些轻描淡写，但该技术在最终效果中起着关键作用。如果没有重新插入，当前的最佳方法将比最简单的启发式方法恶化。在重新插入的同时 即使是随机删除策略也能达到最佳效果。结果，我们第一次系统地重新审视了网络拆除问题中的重新插入功能。我们在由四个经典网络模型生成的两个合成网络上，以及在18个现实世界网络中重新实现并比较了10种启发式和近似竞争方法，这些网络涵盖了七个不同规模的领域。综合消融结果表明：i）HBA（High Betweenness Adaptation，no reinering）是最有效的网络拆卸策略，但是，它仅适用于小型网络。ii）HDA（高适应度，可重新插入）在效果和效率之间达到最佳平衡；iii）重新插入技术有助于提高大多数当前方法的性能；iv）一个 对于大多数方法，最有效的重新插入策略是根据节点加入群集的方式添加节点，从而最大程度地减少数量和大小的乘积。我们的结果可以作为调查参考，以帮助您进一步了解当前的方法，然后设计更好的方法。