Novel network representation model for improving controllability processes on temporal networks

Abstract Temporal networks are known as the most important tools for representing and storing dynamic systems. This type of network accurately demonstrates all the dynamic changes that occur in a dynamic system. In different applications of dynamic systems, different representation of network models has been used to represent temporal networks. In the last decade, controllability in dynamic systems has become one of the most important challenges in this field. Controllability means the transfer of the network from an initial state to a desired final state in a certain period of time. The most common representation of network model used in control processes is the layered model. But this model has a high overhead, and on the other hand, it slows down the network control processes. In this article, we have proposed a new model for storing and representing temporal networks, which uses a tree structure to save all dynamics of network. Considering that in the proposed model only essential network control information is stored, this model has a very low data overhead compared to the layered model, and this makes the control processes run at a higher speed.