English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper we investigate how the modularity of model and real-worldsocial networks affect their robustness and the efficacy of node attack(removal) strategies based on node degree (ID) and node betweenness (IB). Webuild Barabasi-Albert model networks with different modularity by a new ad hocalgorithm that rewire links forming networks with community structure. Wetraced the network robustness using the largest connected component (LCC). Wefind that higher level of modularity decreases the model network robustnessunder both attack strategies, i.e. model network with higher communitystructure showed faster LCC disruption when subjected to node removal. Veryinteresting, we find that when model networks showed non-modular structure orlow modularity, the degree-based (ID) is more effective than thebetweenness-based node attack strategy (IB). Conversely, in the case the modelnetwork present higher modularity, the IB strategies becomes clearly the mosteffective to fragment the LCC. Last, we investigated how the modularity of thenetwork structure evaluated by the modularity indicator (Q) affect therobustness and the efficacy of the attack strategies in 12 real-world socialnetworks. We found that the modularity Q is negatively correlated with therobustness of the real-world social networks under IB node attack strategy(p-value< 0.001). This result indicates how real-world networks with highermodularity (i.e. with higher community structure) may be more fragile tobetwenness-based node attack. The results presented in this paper unveil therole of modularity and community structure for the robustness of networks andmay be useful to select the best node attack strategies in network.

Modularity affects the robustness of scale-free model and real-world social networks under betweenness and degree-based node attack