Characterizing behaviour of Complex networks against perturbations and generation of Pseudo-random networks

Vulnerability of a real-world complex network against unwanted attacks and random link failures is an issue of immense concern. A small attack or failure of the network, has the potential to trigger a global cascading breakdown, thereby raising questions with regard to the possible strategies to combat such a mishap. Many works have been published lately, that deals mainly with the revival of a complex network after an attack or failure. In this paper, we propose to build the network architecture in an efficient manner, so that the network can withstand attacks or link failures up to some certain pre-specified limit. We introduce a novel approach to enhance the robustness of a network from the prevention point of view, that is prior to an attack or failure. Simulation results reveal that with a slight increase in the number of driver nodes, from that obtained using the existing maximum matching algorithm, enhances the stability of the network up to a large number of link failures. We also observe that, the sparse and inhomogeneous networks are difficult to control and are less robust, compared to dense and homogeneous networks.