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 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.

翻译：度保持重连是生成具有给定同配性的无权网络的常用技术，但对于加权网络，如何实现类似操作以保留节点强度及其他关键网络特征（如稀疏度）尚不明确。本研究提出一种对加权网络进行重连以实现特定有向同配性的新方法。该方法采用混合整数规划框架构建具有预定同配性系数的目标网络，随后执行一种名为“强度与稀疏性保持重连”（SSPR）的高效重连算法。SSPR在重连后保留节点强度分布与网络稀疏性，且可通过额外计算代价兼顾边权分布等附加属性。该优化方案可用于确定初始网络可行的同配性范围。通过将所提出的SSPR算法应用于两类经典网络模型，验证了其有效性。