Assortativity measures the tendency of a vertex in a network being connected by other vertexes with respect to some vertex-specific features. Classical assortativity coefficients are defined for unweighted and undirected networks with respect to vertex degree. We propose a class of assortativity coefficients that capture the assortative characteristics and structure of weighted and directed networks more precisely. The vertex-to-vertex strength correlation is used as an example, but the proposed measure can be applied to any pair of vertex-specific features. The effectiveness of the proposed measure is assessed through extensive simulations based on prevalent random network models in comparison with existing assortativity measures. In application World Input-Ouput Networks,the new measures reveal interesting insights that would not be obtained by using existing ones. An implementation is publicly available in a R package "wdnet".

翻译：分布度测量一个网络中由其他顶端连接的顶点与某些顶点特征相连接的趋势。 在顶点程度方面,为无加权和无定向网络界定了典型的分类系数。我们建议了一组反映加权和定向网络的分类特征和结构的分类系数。将顶点对顶点强度的相对关系作为例子使用,但拟议的措施可以适用于任何一对顶点特征。拟议措施的有效性是通过广泛模拟来评估的,其依据是普遍的随机网络模型,与现有的类点度措施相比。在应用世界输入-Ouput网络中,新措施揭示了有趣的洞察力,而利用现有的套件是无法获得的。R软件“wdnet”中可公开提供实施。