2021
Journal article  Open Access

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

Rossetti G., Citraro S., Milli L.

Computer Science - Social and Information Networks  Artificial Intelligence  Homophily  Complex Networks  Assortativity  Computer Networks and Communications  Feature-rich networks  Attributed networks  Mixing patterns 

Unveiling 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 have 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. Different 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.

Source: IEEE intelligent systems 36 (2021): 25–34. doi:10.1109/MIS.2021.3051291

Publisher: IEEE Computer Society,, Los Alamitos, CA , Stati Uniti d'America


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BibTeX entry
@article{oai:it.cnr:prodotti:454273,
	title = {Conformity: a Path-Aware Homophily Measure for Node-Attributed Networks},
	author = {Rossetti G. and Citraro S. and Milli L.},
	publisher = {IEEE Computer Society,, Los Alamitos, CA , Stati Uniti d'America},
	doi = {10.1109/mis.2021.3051291},
	journal = {IEEE intelligent systems},
	volume = {36},
	pages = {25–34},
	year = {2021}
}