  Unveil the homophilic/heterophilic behaviors that characterize the wiring
patterns of complex networks is an important task in social network analysis,
often approached studying the assortative mixing of node attributes. Recent
works underlined that a global measure to quantify node homophily necessarily
provides a partial, often deceiving, picture of the reality. Moving from such
literature, in this work, we propose a novel measure, namely Conformity,
designed to overcome such limitation by providing a node-centric quantification
of assortative mixing patterns. Differently from the measures proposed so far,
Conformity is designed to be path-aware, thus allowing for a more detailed
evaluation of the impact that nodes at different degrees of separations have on
the homophilic embeddedness of a target. Experimental analysis on synthetic and
real data allowed us to observe that Conformity can unveil valuable insights
from node-attributed graphs.
