  One of the interesting phenomena due to the topological heterogeneities in
complex networks is the friendship paradox, stating that your friends have on
average more friends than you do. Recently, this paradox has been generalized
for arbitrary nodal attributes, called a generalized friendship paradox (GFP).
In this paper, we analyze the GFP for the networks in which the attributes of
neighboring nodes are correlated with each other. The correlation structure
between attributes of neighboring nodes is modeled by the
Farlie-Gumbel-Morgenstern copula, enabling us to derive approximate analytical
solutions of the GFP for three kinds of methods summarizing the neighborhood of
the focal node, i.e., mean-based, median-based, and fraction-based methods. The
analytical solutions are comparable to simulation results, while some
systematic deviations between them might be attributed to the higher-order
correlations between nodal attributes. These results help us get deeper insight
into how various summarization methods as well as the correlation structure of
nodal attributes affect the GFP behavior, hence better understand various
related phenomena in complex networks.
