A novel improved structural controllability method on complex temporal networks based on temporal ACO algorithm

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 matc...

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Published inInternational journal of control Vol. 98; no. 9; pp. 2231 - 2244
Main Authors Xia, Zhengwei, Zhang, Feiyun, Li, Na, Arebi, Peyman
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.09.2025
Taylor & Francis Ltd
Subjects
Algorithms
Complex temporal networks
Controllability
driver nodes
Matching
meta-heuristic algorithm
network controllability
Networks
Nodes
temporal ACO algorithm
Online AccessGet full text
ISSN0020-7179
1366-5820
DOI10.1080/00207179.2025.2454916

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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.
AbstractList 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.
Author Li, Na
Zhang, Feiyun
Xia, Zhengwei
Arebi, Peyman
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SubjectTerms Algorithms
Complex temporal networks
Controllability
driver nodes
Matching
meta-heuristic algorithm
network controllability
Networks
Nodes
temporal ACO algorithm
Title A novel improved structural controllability method on complex temporal networks based on temporal ACO algorithm
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