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

Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks