Robustness of self-similar networks with mixture degree distribution

In this paper, we investigate stability of the networks with topological self-similar structure that emerges by a mixture of both algebraic and exponential degree distributions in a wide range of parameter values. These networks interpose between exponential networks and scale-free networks. We find that these networks are robust under random failures and fragile under intentional attacks. Interestingly, the underlying fractal property introduces robustness against intentional attacks with respect to scale-free networks. Analytical and experimental results indicate that such networks have overall better performance than random networks and scale-free networks to both random failures and intentional attacks.