Improving the efficiency of network controllability processes on temporal networks

Abstract

The controllability of temporal networks has become an important issue for researchers in recent years. In temporal networks, the controllability is achieved through the control of dynamics. Existing studies take only some dynamics into account when controlling such networks, e.g., only adding links, and ignore the rest. Besides, conventional controllability methods on temporal networks suffer from time complexity and data overhead. In this paper, a dynamic controllability method is proposed which considers all dynamics of temporal networks to fully control the network, including adding and removing nodes and links. The proposed controllability method finds all the minimum driver nodes sets (MDSs) of a temporal network in a polynomial time. In this regard, to maintain and track the network changes, a new tree-based model, named Controllable Dynamics Temporal Network (CDTN), is proposed by which the control process is executed with high speed and low overhead. The experimental results show that the proposed controllability method outperforms state-of-the-art methods in terms of speed (47% improvement), overhead (33% improvement), and the size of MDS (38% improvement).