利用Copula函数生成度相关网络
Generation of degree-correlated networks using copulas
摘要：
复杂网络上的动态过程，如信息传播、创新扩散、连锁故障或传染病传播，都受到其底层拓扑结构的高度影响，例如，度-度相关性。在这里，我们引入连接的概念，以人为地生成随机网络与任意度分布和丰富的先验度度相关（或“关联”）结构。所提出的形式主义和相应的算法的准确性进行了数值验证。导出的网络集成可以系统地部署为适当的零模型，以揭示真实网络的拓扑结构与其上的动态之间的复杂相互作用。
英文摘要：
Dynamical processes on complex networks such as information propagation, innovation diffusion, cascading failures or epidemic spreading are highly affected by their underlying topologies as characterized by, for instance, degree-degree correlations. Here, we introduce the concept of copulas in order to artificially generate random networks with an arbitrary degree distribution and a rich a priori degree-degree correlation (or `association') structure. The accuracy of the proposed formalism and corresponding algorithm is numerically confirmed. The derived network ensembles can be systematically deployed as proper null models, in order to unfold the complex interplay between the topology of real networks and the dynamics on top of them.
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