A novel controllability method on complex temporal networks based on temporal motifs

Abstract

Complex temporal networks have become instrumental in modeling dynamic systems across various disciplines, presenting unique challenges and opportunities in understanding and influencing their behavior. Controllability, a fundamental aspect of network dynamics, plays a pivotal role in manipulating these systems towards desired states. Temporal motifs are important patterns in temporal complex networks that have many applications in solving problems related to this type of networks. In this paper, a novel method for controlling temporal complex networks using temporal motifs is proposed. First, the most important effective temporal motifs in the controllability processes of complex networks have been identified and it has been shown that the network can be fully controlled using these temporal motifs. Then, an algorithm for extracting temporal motifs is proposed. This algorithm has been proposed to identify effective temporal motifs in network controllability to optimally identify control nodes. To increase the efficiency of extracting temporal motifs, a method for predicting the temporal motif-based link has been proposed, which predicts temporal motifs. The results of the simulation of the proposed method based on temporal motifs and its implementation on real-world temporal complex networks demonstrates that its performance was better than the conventional controllability methods.