TY - JOUR T1 - Conformity: a Path-Aware Homophily measure for Node-Attributed Networks JF - IEEE Intelligent SystemsIEEE Intelligent Systems Y1 - 2021 A1 - Giulio Rossetti A1 - Salvatore Citraro A1 - Letizia Milli AB - 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. SN - 1941-1294 UR - https://ieeexplore.ieee.org/document/9321348 JO - IEEE Intelligent Systems ER -