Empirical determination of the optimal attack for fragmentation of modular networks

Carolina de Abreu, Sebastián Gonçalves and Bruno Requião da Cunha

Physica A: Statistical Mechanics and its Applications, 2021, vol. 563, issue C

Abstract: We perform all possible removals of n nodes from networks of size N, 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 N vertices), limited to the removal of n nodes, and the set that produces such damage is called the optimal set of size n. 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 n=5 on networks of size N=100, because the number of all possible sets (≈108) 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 Qc≈0.73 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 Qc, all the heuristic strategies studied have a similar performance to a random attack.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037843712030786X
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:563:y:2021:i:c:s037843712030786x

DOI: 10.1016/j.physa.2020.125486

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().