  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".
