ESTIMATING AND ENHANCING THE FEEDBACKABILITY OF COMPLEX NETWORKS

Chengyi Tu () and Ruiyang Yan
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Chengyi Tu: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
Ruiyang Yan: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China

Advances in Complex Systems (ACS), 2018, vol. 21, issue 02, 1-17

Abstract: In this paper, we explore the relationship between Minimum Feedbackability Set (MFS) and Feedbackability Set (FS) of complex networks and use simulated annealing algorithm to obtain a relatively fast and reliable estimation of the MFS. Then, we implement this method to estimate the MFS of some real-world networks and explore whether the topological features influence the size of MFS. Additionally, we propose an optimal perturbation strategy to enhance the feedbackability of complex networks. Finally, we also research the intricate relationship between the structural properties and the feedbackability of various networks perturbed by optimal and random perturbation strategies.

Keywords: Complex network; simulated annealing algorithm; estimating feedbackability; enhancing feedbackability (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0219525918500054

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