标题： 基于适合度模型的复杂网络拓扑结构与系统性风险的自举法研究 显示英文标题

标题： Bootstrapping topology and systemic risk of complex network using the fitness model

摘要： 我们提出了一种新颖的方法，从部分信息中重构复杂网络。我们假设只知道节点子集的链接，并且知道每个节点的一些非拓扑性质（适应度）。缺失的链接根据适应度模型生成，该模型针对已知链接的节点子集进行了校准。我们测量了若干拓扑性质的重构质量，例如网络密度和度分布作为初始节点子集大小的函数。此外，我们还研究了网络对压力传播的韧性。我们首先在使用指数随机图模型生成的一组合成网络上测试该方法，这允许应用统计力学中的常用工具。然后我们在世界贸易网络的实际案例中测试它。在这两种情况下，我们都发现 10% 的节点子集就足以以 5% 的误差重构网络的主要特征及其韧性。 显示英文摘要

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%.