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...

Descripción completa

Guardado en:
Detalles Bibliográficos
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!
id oai:doaj.org-article:1666368bcdd64d0b99f110fbfe15a898
record_format dspace
spelling oai:doaj.org-article:1666368bcdd64d0b99f110fbfe15a8982021-11-07T12:19:01ZModularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack10.1007/s41109-021-00426-y2364-8228https://doaj.org/article/1666368bcdd64d0b99f110fbfe15a8982021-11-01T00:00:00Zhttps://doi.org/10.1007/s41109-021-00426-yhttps://doaj.org/toc/2364-8228Abstract 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 new ad hoc algorithm that rewire links forming networks with community structure. We traced the network robustness using the largest connected component (LCC). We find that when model networks present absent or low modular structure ID strategy is more effective than IB to decrease the LCC. Conversely, in the case the model network present higher modularity, the IB strategy becomes the most effective to fragment the LCC. In addition, networks with higher modularity present a signature of a 1st order percolation transition and a decrease of the LCC with one or several abrupt changes when nodes are removed, for both strategies; differently, networks with non-modular structure or low modularity show a 2nd order percolation transition networks when nodes are removed. Last, we investigated how the modularity of the network structure evaluated by the modularity indicator (Q) affect the network robustness and the efficacy of the attack strategies in 12 real-world social networks. We found that the modularity Q is negatively correlated with the robustness of the real-world social networks for both the node attack strategies, especially for the IB strategy (p-value < 0.001). This result indicates how real-world networks with higher modularity (i.e. with higher community structure) may be more fragile to node attack. The results presented in this paper unveil the role of modularity and community structure for the robustness of networks and may be useful to select the best node attack strategies in network.Quang NguyenTuan V. VuHanh-Duyen DinhDavide CassiFrancesco ScotognellaRoberto AlfieriMichele BellingeriSpringerOpenarticleNetwork robustnessModular networkNode attack strategyCentrality measuresApplied mathematics. Quantitative methodsT57-57.97ENApplied Network Science, Vol 6, Iss 1, Pp 1-21 (2021)
institution DOAJ
collection DOAJ
language EN
topic Network robustness
Modular network
Node attack strategy
Centrality measures
Applied mathematics. Quantitative methods
T57-57.97
spellingShingle Network robustness
Modular network
Node attack strategy
Centrality measures
Applied mathematics. Quantitative methods
T57-57.97
Quang Nguyen
Tuan V. Vu
Hanh-Duyen Dinh
Davide Cassi
Francesco Scotognella
Roberto Alfieri
Michele Bellingeri
Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack
description 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 new ad hoc algorithm that rewire links forming networks with community structure. We traced the network robustness using the largest connected component (LCC). We find that when model networks present absent or low modular structure ID strategy is more effective than IB to decrease the LCC. Conversely, in the case the model network present higher modularity, the IB strategy becomes the most effective to fragment the LCC. In addition, networks with higher modularity present a signature of a 1st order percolation transition and a decrease of the LCC with one or several abrupt changes when nodes are removed, for both strategies; differently, networks with non-modular structure or low modularity show a 2nd order percolation transition networks when nodes are removed. Last, we investigated how the modularity of the network structure evaluated by the modularity indicator (Q) affect the network robustness and the efficacy of the attack strategies in 12 real-world social networks. We found that the modularity Q is negatively correlated with the robustness of the real-world social networks for both the node attack strategies, especially for the IB strategy (p-value < 0.001). This result indicates how real-world networks with higher modularity (i.e. with higher community structure) may be more fragile to node attack. The results presented in this paper unveil the role of modularity and community structure for the robustness of networks and may be useful to select the best node attack strategies in network.
format article
author Quang Nguyen
Tuan V. Vu
Hanh-Duyen Dinh
Davide Cassi
Francesco Scotognella
Roberto Alfieri
Michele Bellingeri
author_facet Quang Nguyen
Tuan V. Vu
Hanh-Duyen Dinh
Davide Cassi
Francesco Scotognella
Roberto Alfieri
Michele Bellingeri
author_sort Quang Nguyen
title Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack
title_short Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack
title_full Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack
title_fullStr Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack
title_full_unstemmed Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack
title_sort modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack
publisher SpringerOpen
publishDate 2021
url https://doaj.org/article/1666368bcdd64d0b99f110fbfe15a898
work_keys_str_mv AT quangnguyen modularityaffectstherobustnessofscalefreemodelandrealworldsocialnetworksunderbetweennessanddegreebasednodeattack
AT tuanvvu modularityaffectstherobustnessofscalefreemodelandrealworldsocialnetworksunderbetweennessanddegreebasednodeattack
AT hanhduyendinh modularityaffectstherobustnessofscalefreemodelandrealworldsocialnetworksunderbetweennessanddegreebasednodeattack
AT davidecassi modularityaffectstherobustnessofscalefreemodelandrealworldsocialnetworksunderbetweennessanddegreebasednodeattack
AT francescoscotognella modularityaffectstherobustnessofscalefreemodelandrealworldsocialnetworksunderbetweennessanddegreebasednodeattack
AT robertoalfieri modularityaffectstherobustnessofscalefreemodelandrealworldsocialnetworksunderbetweennessanddegreebasednodeattack
AT michelebellingeri modularityaffectstherobustnessofscalefreemodelandrealworldsocialnetworksunderbetweennessanddegreebasednodeattack
_version_ 1718443471046443008