  A heterogeneous structure of social networks induces various intriguing
phenomena. One of them is the friendship paradox, which states that on average
your friends have more friends than you do. Its generalization, called the
generalized friendship paradox (GFP), states that on average your friends have
higher attributes than yours. Despite successful demonstrations of the GFP by
empirical analyses and numerical simulations, analytical, rigorous
understanding of the GFP has been largely unexplored. Recently, an analytical
solution for the probability that the GFP holds for an individual in a network
with correlated attributes was obtained using the copula method but by assuming
a locally tree structure of the underlying network [Jo~et~al., Physical Review
E~\textbf{104}, 054301 (2021)]. Considering the abundant triangles in most
social networks, we employ a vine copula method to incorporate the attribute
correlation structure between neighbors of a focal individual in addition to
the correlation between the focal individual and its neighbors. Our analytical
approach helps us rigorously understand the GFP in more general networks such
as clustered networks and other related interesting phenomena in social
networks.
