Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack
Abstract In this paper we investigate how the modularity of model and real-world social networks affect their robustness and the efficacy of node attack (removal) strategies based on node degree (ID) and node betweenness (IB). We build Barabasi–Albert model networks with different modularity by a ne...
Guardado en:
Autores principales: | Quang Nguyen, Tuan V. Vu, Hanh-Duyen Dinh, Davide Cassi, Francesco Scotognella, Roberto Alfieri, Michele Bellingeri |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
SpringerOpen
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/1666368bcdd64d0b99f110fbfe15a898 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Simulating systematic bias in attributed social networks and its effect on rankings of minority nodes
por: Leonie Neuhäuser, et al.
Publicado: (2021) -
Global and local community memberships for estimating spreading capability of nodes in social networks
por: Simon Krukowski, et al.
Publicado: (2021) -
Graph convolutional and attention models for entity classification in multilayer networks
por: Lorenzo Zangari, et al.
Publicado: (2021) -
Editorial Board
Publicado: (2021) -
Balancing capacity and epidemic spread in the global airline network
por: Robert Harper, et al.
Publicado: (2021)