Degree-preserving rewiring is a widely used technique for generating unweighted networks with given assortativity, but for weighted networks, it is unclear how an analog would preserve the strengths and other critical network features such as sparsity level. This study introduces a novel approach for rewiring weighted networks to achieve desired directed assortativity. The method utilizes a mixed integer programming framework to establish a target network with predetermined assortativity coefficients, followed by an efficient rewiring algorithm termed “strength and sparsity preserving rewiring” (SSPR). SSPR retains the node strength distributions and network sparsity after rewiring. It is also possible to accommodate additional properties like edge weight distribution, albeit with extra computational cost. The optimization scheme can be used to determine feasible assortativity ranges for an initial network. The effectiveness of the proposed SSPR algorithm is demonstrated through its application to two classes of popular network models.

中文翻译：

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一种强度和稀疏性保持算法，用于生成具有预定分类性的加权有向网络

保留度重连是一种广泛使用的技术，用于生成具有给定分类性的未加权网络，但对于加权网络，尚不清楚模拟如何保留强度和其他关键网络特征（例如稀疏程度）。这项研究引入了一种重新连接加权网络以实现所需的定向分类的新方法。该方法利用混合整数规划框架建立具有预定相配系数的目标网络，然后采用称为“强度和稀疏性保留重新布线”（SSPR）的有效重新布线算法。SSPR 在重新布线后保留了节点强度分布和网络稀疏性。尽管需要额外的计算成本，但也可以容纳其他属性，例如边缘权重分布。优化方案可用于确定初始网络的可行搭配范围。所提出的 SSPR 算法的有效性通过其在两类流行网络模型中的应用得到了证明。