Abstract
The controllability of complex temporal networks is an area of research focused on understanding how to guide or influence the behaviour of dynamic. Structural controllability is considered as one of the most prominent network controllability methods. Structural controllability uses the maximum matching algorithm to find the minimum set of control nodes. The maximum matching algorithm on temporal networks is a class of NP-hard problems. In this paper, a novel method based on temporal ACO algorithm is proposed to solve the maximum matching problem in structural controllability. The ACO algorithm has been adapted to temporal networks. The results of implementing the proposed method on real-world datasets demonstrate that the ACO algorithm has a good performance and has converged to the optimal solution with high speed. The results demonstrate that the proposed method has higher efficiency in finding driver nodes and algorithm execution speed compared to the basic structural controllability.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call