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<p>This paper extends Compositional Equivalence – which is a structural correspondence type intended for multiplex networks – by incorporating actor attributes in the modeling of the network relational structure as diagonal matrices. As an illustration, we construct the positional system of the Florentine families network with Business and Marriage ties together with relevant characteristics acquired from the actors such as the families’ financial Wealth and the number of Priorates they held. Different representations of the cumulated person hierarchies reveal that adding Wealth to the modeling provides a more accurate picture of what the substantial narrative says about this network.</p>
</abstract>

This paper extends Compositional Equivalence – which is a structural correspondence type intended for multiplex networks – by incorporating actor attributes in the modeling of the network relational structure as diagonal matrices. As an illustration, we construct the positional system of the Florentine families network with Business and Marriage ties together with relevant characteristics acquired from the actors such as the families’ financial Wealth and the number of Priorates they held. Different representations of the cumulated person hierarchies reveal that adding Wealth to the modeling provides a more accurate picture of what the substantial narrative says about this network.

Compositional Equivalence with Actor Attributes: Positional Analysis of the Florentine Families Network

Connections

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper extends Compositional Equivalence – which is a structural correspondence type intended for multiplex networks – by incorporating actor attributes in...

Compositional Equivalence with Actor Attributes: Positional Analysis of the Florentine Families Network

Publicado en línea: 04 jun 2018

Páginas: 53 - 68

DOI: https://doi.org/10.21307/connections-2017-004

© 2021 J. Antonio Rivero Ostoic et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.