Robustness enhancement of complex networks via No-Regret learning

Optimizing complex networks to be resilient against various attack models has been an important problem that is actively studied in the academia. In the proposed optimization method, individual node degrees are balanced iteratively based on the No-Regret learning algorithm, resulting in a robust network topology with increased resilience against outside attacks. Through simulation results, we show that the proposed robustness enhanced networks perform well under targeted attacks compared to the conventional optimized networks.