论文标题
一种有效的算法，用于生成具有预定分类度量的有向网络
An Efficient Algorithm for Generating Directed Networks with Predetermined Assortativity Measures
论文作者
论文摘要
分类系数是分析定向和无向网络的重要指标。通常，不能保证拟合的模型始终与给定网络中的分类系数一致，并且有向网络的结构比无方向性的网络更为复杂。因此，我们通过提出一种称为DIDPR的学位的重新布线算法来提供一种补救措施，用于生成具有给定的定向分类系数的定向网络。我们通过同时考虑四个定向的分类系数来构建目标网络的关节边缘分布，前提是它们是可以实现的，并通过解决凸优化问题来获得所需的网络。我们的算法还有助于检查给定的分类系数的可达到性。我们通过对两个不同的网络模型（即erdös--rényi和优先附着随机网络）进行模拟研究来评估所提出的算法的性能。然后，我们将算法应用于Facebook Wall Post网络，作为真实数据示例。实施我们的算法的代码在R软件包WDNET中公开可用。
Assortativity coefficients are important metrics to analyze both directed and undirected networks. In general, it is not guaranteed that the fitted model will always agree with the assortativity coefficients in the given network, and the structure of directed networks is more complicated than the undirected ones. Therefore, we provide a remedy by proposing a degree-preserving rewiring algorithm, called DiDPR, for generating directed networks with given directed assortativity coefficients. We construct the joint edge distribution of the target network by accounting for the four directed assortativity coefficients simultaneously, provided that they are attainable, and obtain the desired network by solving a convex optimization problem.Our algorithm also helps check the attainability of the given assortativity coefficients. We assess the performance of the proposed algorithm by simulation studies with focus on two different network models, namely Erdös--Rényi and preferential attachment random networks. We then apply the algorithm to a Facebook wall post network as a real data example. The codes for implementing our algorithm are publicly available in R package wdnet.
