Network fault tolerance (also known as resilience or robustness) is becoming a highly relevant topic, expecially in real networks, where it is essential to know to what extent it is still working notwithstanding its failures. Different questions need attention to guarantee robustness, as how it can be effectively and efficiently (i.e. rapidly) assessed, and which factors it depends on, as network structure, network dynamics and failure mechanisms. All studies aim at finding a way to hold (or increase) resilience; in this work we propose a strategy to improve robustness for Scale-free networks by adding links between highly distant nodes in the network; results show that even adding few long-distance links leads to a significant improvement of resilience, therefore this can be assumed as an effective (and possibly with low cost) approach for increasing robustness in networks.

Exploiting Long Distance Connections to Strengthen Network Robustness

Carchiolo, V.;Longheu, A.;Malgeri, M.;Mangioni, G.
2018-01-01

Abstract

Network fault tolerance (also known as resilience or robustness) is becoming a highly relevant topic, expecially in real networks, where it is essential to know to what extent it is still working notwithstanding its failures. Different questions need attention to guarantee robustness, as how it can be effectively and efficiently (i.e. rapidly) assessed, and which factors it depends on, as network structure, network dynamics and failure mechanisms. All studies aim at finding a way to hold (or increase) resilience; in this work we propose a strategy to improve robustness for Scale-free networks by adding links between highly distant nodes in the network; results show that even adding few long-distance links leads to a significant improvement of resilience, therefore this can be assumed as an effective (and possibly with low cost) approach for increasing robustness in networks.
9783030027377
Complex networks; Failure; Resilience; Robustness; Scale-free networks; Theoretical Computer Science; Computer Science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/361758
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