2021/01/13 by Yelie Yuan, Jun Yan, Yuan, Yelie +3
Neuroscience · Physics and Astronomy · #Applications (stat.AP) #Complex Network Analysis Techniques #FOS: Computer and information sciences #Functional Brain Connectivity Studies
paper · pdf · doi:10.48550/arxiv.2101.05389
openalex publication_date 2021/01/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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".