Robustness of complex networks to cascading failures induced by Poisson fluctuating loads

Considering the fact that overload failures in real-world functional networks are usually caused by extreme values of temporally fluctuating loads that exceed the allowable range, we study the robustness of complex networks against cascading overload failures in such situation. In our model, at each cascading step the fluctuating load on a network is described by the total number of walkers, which is a random number that subjects to Poisson distribution. And a node fails when its overload probability is beyond its risk tolerance. Relative size of the giant component as a function of the cascade step is used to assess the robustness of networks against cascading overload. Comparing to the cases of stationary overload probability in Kishore et al., (2011, 2012) and previous study on temporally fluctuating (the total load reduced in accordance with the reduction of the network size during a cascade process) overload probability in Mizutaka and Yakubo, (2015), we find that the total fluctuating loads that subject to random distribution at each cascade process induce remarkable difference for extreme events probability distribution. The study shows that the change in the property of the load fluctuations may drastically alter the dependence degree of the overload probability and hence the robustness of networks. Furthermore, our results obtained through intensive simulations show that scale-free networks are more fragile against cascading overload failures than ER and WS networks. This conclusion is contrary to those previous prediction without considering random fluctuating loads and endogenous risk resistance ability. (C) 2019 Elsevier B.V. All rights reserved.