Emergent Mind

Network Overload due to Massive Attacks

(1802.03901)
Published Feb 12, 2018 in physics.soc-ph and cs.SI

Abstract

We study the cascading failure of networks due to overload, using the betweenness centrality of a node as the measure of its load following the Motter and Lai model. We study the fraction of survived nodes at the end of the cascade $pf$ as function of the strength of the initial attack, measured by the fraction of nodes $p$, which survive the initial attack for different values of tolerance $\alpha$ in random regular and Erd\"os-Renyi graphs. We find the existence of first order phase transition line $pt(\alpha)$ on a $p-\alpha$ plane, such that if $p <p_t$ the cascade of failures lead to a very small fraction of survived nodes $p_f$ and the giant component of the network disappears, while for $p>pt$, $pf$ is large and the giant component of the network is still present. Exactly at $pt$ the function $pf(p)$ undergoes a first order discontinuity. We find that the line $pt(\alpha)$ ends at critical point $(pc,\alphac)$ ,in which the cascading failures are replaced by a second order percolation transition. We analytically find the average betweenness of nodes with different degrees before and after the initial attack, investigate their roles in the cascading failures, and find a lower bound for $pt(\alpha)$. We also study the difference between a localized and random attacks.

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