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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Empirical determination of the optimal attack for fragmentation of modular networks
Organization Unit
  • Carolina de Abreu
  • Sebastián Gonçalves
  • Bruno Requião da Cunha
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Physica A: Statistical Mechanics and its Applications
Publisher Elsevier
Geographical Reach international
ISSN 0378-4371
Volume 563
Page Range 125486
Date 2021
Abstract Text We perform all possible removals of nodes from networks of size , then we identify and measure the largest connected component left in every case. The smallest of these components represents the maximum possible damage (on a network of vertices), limited to the removal of nodes, and the set that produces such damage is called the optimal set of size . We apply the procedure in a series of networks with controlled and varied modularity. Then, we compare the resulting statistics with the effect of removing the same amount of vertices according to state of the art methods of network fragmentation, i.e., High Betweenness Adaptive attack, Collective Influence, and Module-Based Attack. For practical matters we performed mainly attacks of size on networks of size , because the number of all possible sets () is at the verge of the computational capability of standard desktops. The results show, in general, that the resilience of networks to attacks has an inverse relationship with modularity, with being the critical value, from which the damage of the optimal attack increases rapidly. Networks are highly vulnerable to targeted attacks when the modularity is greater than the critical value of each heuristic method. On the other hand, for modularities lower than , all the heuristic strategies studied have a similar performance to a random attack.
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Digital Object Identifier 10.1016/j.physa.2020.125486
Other Identification Number merlin-id:19966
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