Node and edge nonlinear eigenvector centrality for hypergraphs
Evaluating the importance of nodes and hyperedges in hypergraphs is relevant to link detection, link prediction and matrix completion. Here, the authors define a family of nonlinear eigenvector centrality measures for both edges and nodes in hypergraphs, propose an algorithm to calculate them, and i...
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/1223cb91ad414a9d8ddc81a142c1d18a |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Evaluating the importance of nodes and hyperedges in hypergraphs is relevant to link detection, link prediction and matrix completion. Here, the authors define a family of nonlinear eigenvector centrality measures for both edges and nodes in hypergraphs, propose an algorithm to calculate them, and illustrate their application on real-world data sets. |
|---|