Detecting informative higher-order interactions in statistically validated hypergraphs

The increasing availability of new data on biological and sociotechnical systems highlights the importance of well grounded filtering techniques to separate meaningful interactions from noise. In this work the authors propose the first method to detect informative connections of any order in statist...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Federico Musciotto, Federico Battiston, Rosario N. Mantegna
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Acceso en línea:https://doaj.org/article/1ce10e574e424627ab206e9d3d8dedc3
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:The increasing availability of new data on biological and sociotechnical systems highlights the importance of well grounded filtering techniques to separate meaningful interactions from noise. In this work the authors propose the first method to detect informative connections of any order in statistically validated hypergraphs, showing on synthetic benchmarks and real-world systems that the highlighted hyperlinks are more informative than those extracted with traditional pairwise approaches.