Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks
Autor: | Letizia Milli, Salvatore Citraro, Giulio Rossetti |
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Rok vydání: | 2021 |
Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Theoretical computer science Computer Networks and Communications Computer science Node (networking) media_common.quotation_subject Mixing patterns Attributed networks Computer Science - Social and Information Networks 02 engineering and technology Complex network Homophily Conformity Measure (mathematics) Artificial Intelligence Path (graph theory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Assortative mixing Social network analysis media_common |
Zdroj: | IEEE Intelligent Systems IEEE intelligent systems 36 (2021): 25–34. doi:10.1109/MIS.2021.3051291 info:cnr-pdr/source/autori:Rossetti G.; Citraro S.; Milli L./titolo:Conformity: a Path-Aware Homophily Measure for Node-Attributed Networks/doi:10.1109%2FMIS.2021.3051291/rivista:IEEE intelligent systems/anno:2021/pagina_da:25/pagina_a:34/intervallo_pagine:25–34/volume:36 |
ISSN: | 1941-1294 1541-1672 |
Popis: | 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. Comment: Submitted to IEEE Intelligent Systems |
Databáze: | OpenAIRE |
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