Center for Information and Communication Technology, Fondazione Bruno Kessler, Povo, TN, Italy.
Nat Commun. 2021 Apr 30;12(1):2478. doi: 10.1038/s41467-021-22721-z.
Percolation is an emblematic model to assess the robustness of interconnected systems when some of their components are corrupted. It is usually investigated in simple scenarios, such as the removal of the system's units in random order, or sequentially ordered by specific topological descriptors. However, in the vast majority of empirical applications, it is required to dismantle the network following more sophisticated protocols, for instance, by combining topological properties and non-topological node metadata. We propose a novel mathematical framework to fill this gap: networks are enriched with features and their nodes are removed according to the importance in the feature space. We consider features of different nature, from ones related to the network construction to ones related to dynamical processes such as epidemic spreading. Our framework not only provides a natural generalization of percolation but, more importantly, offers an accurate way to test the robustness of networks in realistic scenarios.
渗流是评估互联系统鲁棒性的一个典型模型，当系统的某些组件受到损坏时，就会发生这种情况。它通常在简单的场景中进行研究，例如随机顺序或按特定拓扑描述符顺序移除系统的单元。然而，在绝大多数实际应用中，需要根据更复杂的协议来拆除网络，例如，结合拓扑属性和非拓扑节点元数据。我们提出了一个新的数学框架来填补这一空白：网络被赋予了特征，根据特征空间中的重要性来移除节点。我们考虑了不同性质的特征，从与网络构建相关的特征到与流行病传播等动态过程相关的特征。我们的框架不仅提供了渗流的自然推广，更重要的是，为在现实场景中测试网络的鲁棒性提供了一种准确的方法。
