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

TL;DR

This paper introduces Conformity, a novel path-aware, node-centric measure for quantifying homophily in networks, providing more detailed insights than traditional global measures.

Contribution

The paper proposes Conformity, a new measure that captures local and path-aware homophily patterns, addressing limitations of existing global metrics.

Findings

Conformity reveals detailed homophily patterns in synthetic and real networks.

It outperforms traditional measures in capturing node-specific mixing behaviors.

Experimental results demonstrate its effectiveness in understanding network wiring patterns.

Abstract

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.