We introduce a methodology based on averaging similarity matrices with the aim of integrating the layers of a multiplex network into a single monoplex network. Multiplex networks are adopted for modelling a wide variety of real-world frameworks, such as multi-type relations in social, economic and biological structures. More specifically, multiplex networks are used when relations of different nature (layers) arise between a set of elements from a given population (nodes). A possible approach for analyzing multiplex similarity networks consists in aggregating the different layers in a single network (monoplex) which is a valid representation—in some sense—of all the layers. In order to obtain such an aggregated network, we propose a theoretical approach—along with its practical implementation—which stems on the concept of similarity matrix average. This methodology is finally applied to a multiplex similarity network of statistical journals, where the three considered layers express the similarity of the journals based on co-citations, common authors and common editors, respectively.
Baccini, F., Barabesi, L., Petrovich, E. (2023). Similarity matrix average for aggregating multiplex networks. JOURNAL OF PHYSICS. COMPLEXITY, 4(2), 1-17 [10.1088/2632-072X/acda09].
Similarity matrix average for aggregating multiplex networks
Baccini, Federica;Barabesi, Lucio;Petrovich, Eugenio
2023-01-01
Abstract
We introduce a methodology based on averaging similarity matrices with the aim of integrating the layers of a multiplex network into a single monoplex network. Multiplex networks are adopted for modelling a wide variety of real-world frameworks, such as multi-type relations in social, economic and biological structures. More specifically, multiplex networks are used when relations of different nature (layers) arise between a set of elements from a given population (nodes). A possible approach for analyzing multiplex similarity networks consists in aggregating the different layers in a single network (monoplex) which is a valid representation—in some sense—of all the layers. In order to obtain such an aggregated network, we propose a theoretical approach—along with its practical implementation—which stems on the concept of similarity matrix average. This methodology is finally applied to a multiplex similarity network of statistical journals, where the three considered layers express the similarity of the journals based on co-citations, common authors and common editors, respectively.File | Dimensione | Formato | |
---|---|---|---|
Baccini_2023_J._Phys._Complex._4_025017.pdf
accesso aperto
Tipologia:
PDF editoriale
Licenza:
Creative commons
Dimensione
2.78 MB
Formato
Adobe PDF
|
2.78 MB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11365/1254635