Improving the controllability robustness of complex temporal networks against intelligent attacks

Abstract The main goal of controllability network methods on complex temporal networks is to control all nodes with the minimum number of control nodes. Real-world complex temporal networks are faced with many errors and attacks that cause the network structure to be changed in some way so that the controllability processes are disturbed and after that, the controllability robustness of the network decreases. One of the most important attacks on complex temporal networks is intelligent attacks. In this paper, the types of intelligent attacks and their destructive effects on the controllability of complex temporal networks have been investigated. In order to increase the controllability robustness of the network against intelligent attacks, a novel graph model and strategies have been proposed on complex dynamic graph by adding new control nodes or adding new links to the network so that the network is protected against intelligent attacks. The results of simulation and comparing them with conventional methods demonstrate that the proposed node addition strategy has performed better than other methods and the improvement rate in terms of execution time is 60%. On the other hand, the proposed immunization strategy by adding links has kept the network controllable with a smaller number of links (38%) and less execution time (52%) compared to other methods.