Robustness of power-law networks: its assessment and optimization

Huiling Zhang (), Yilin Shen () and My T. Thai ()
Additional contact information
Huiling Zhang: University of Florida
Yilin Shen: University of Florida
My T. Thai: University of Florida

Journal of Combinatorial Optimization, 2016, vol. 32, issue 3, No 3, 696-720

Abstract: Abstract Many practical complex networks, such as the Internet, WWW and social networks, are discovered to follow power-law distribution in their degree sequences, i.e., the number of nodes with degree $$i$$ i in these networks is proportional to $$i^{-\beta }$$ i - β for some exponential factor $$\beta > 0$$ β > 0 . However, these networks also expose their vulnerabilities to a great number of threats such as adversarial attacks on the Internet, cyber-crimes on the WWW or malware propagations on social networks. Although power-law networks have been found robust under random attacks and vulnerable to intentional attacks via experimental observations, how to better understand their vulnerabilities from a theoretical point of view still remains open. In this paper, we study the vulnerability of power-law networks under random attacks and adversarial attacks using the in-depth probabilistic analysis on the theory of random power-law graph models. Our results indicate that power-law networks are able to tolerate random failures if their exponential factor $$\beta $$ β is $$ 2.5$$ β > 2.5 the network robustness is unpredictable since it depends on the specific attacking strategy.

Keywords: Power-law networks; Robustness; Probabilistic analysis; Optimization (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10878-015-9893-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:jcomop:v:32:y:2016:i:3:d:10.1007_s10878-015-9893-7

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878

DOI: 10.1007/s10878-015-9893-7

Access Statistics for this article

Journal of Combinatorial Optimization is currently edited by Thai, My T.

More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().