An Effective Approach Based on Temporal Centrality Measures for Improving Temporal Network Controllability

The Controllability on temporal complex networks is one of the most important challenges among researchers in this field. The primary purpose of network controllability is to apply inputs by selecting minimum driver nodes set (MDS) to the network components to move the network from an initial state to a final state in a limited time. The most important challenges in the controllability of temporal networks can be mentioned the high complexity of the control algorithms used in these methods as well as the high data overhead of temporal network representation models such as the layered model. In this paper, centrality measures are used as the most important characteristics of networks for network controllability. For this purpose, centrality measures have been redefined based on temporal networks and a new controllability method has been proposed based on temporal centrality measures. Then, these properties are used for selecting the minimal driver nodes set, in such a way that the network can be fully controlled using these nodes. The experimental results demonstrate that by using temporal centrality measures the execution speed of control processes is improved (57% improvement) and the overhead is not increased and also the control process has led to the same length of MDS as other conventional controllability methods, it has even been better in some cases.