基于适应度模型的复杂网络自举拓扑与系统风险
Bootstrapping topology and systemic risk of complex network using the fitness model
摘要：
提出了一种基于部分信息重构复杂网络的新方法。我们假设只知道一个子集的节点的链接，并知道一些非拓扑量（健身）表征每个节点。根据在已知链路的节点子集上校准的适应度模型，在后一数量的基础上生成缺失链路。我们测量的质量重建的几个拓扑性质，如网络密度和度分布的函数的初始子集的节点的大小。此外，我们还研究了网络的灾难传播的弹性。我们首先在使用指数随机图模型生成的合成网络集合上测试该方法，该模型允许应用来自统计力学的常用工具。然后，我们在世界贸易网的实证案例中进行了测试。在这两种情况下，我们发现10%的节点子集足以重建网络的主要特征及其弹性，误差为5%。
英文摘要：
We present a novel method to reconstruct complex network from partial information. We assume to know the links only for a subset of the nodes and to know some non-topological quantity (fitness) characterising every node. The missing links are generated on the basis of the latter quan- tity according to a fitness model calibrated on the subset of nodes for which links are known. We measure the quality of the reconstruction of several topological properties, such as the network density and the degree distri- bution as a function of the size of the initial subset of nodes. Moreover, we also study the resilience of the network to distress propagation. We first test the method on ensembles of synthetic networks generated with the Exponential Random Graph model which allows to apply common tools from statistical mechanics. We then test it on the empirical case of the World Trade Web. In both cases, we find that a subset of 10 % of nodes is enough to reconstruct the main features of the network along with its resilience with an error of 5%.
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