A novel controllability method on temporal networks based on tree model
Abstract 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...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-024-05883-5 |
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| author | Peyman Arebi |
| author_facet | Peyman Arebi |
| author_sort | Peyman Arebi |
| collection | DOAJ |
| description | Abstract 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. In this paper, we embark on a comprehensive exploration of controllability within the realm of temporal networks. A new method for controlling temporal networks is proposed, in which the intervention of all the dynamics of temporal networks can provide the possibility to speed up the network controllability processes. In the proposed method, the network dynamics are stored in the tree data structure to reduce the computational complexity of the algorithm for finding control nodes while maintaining essential information in controllable processes. Results show that the proposed algorithm with linear complexity of $${\varvec{O}}({{\varvec{N}}}^{2}{\varvec{l}}{\varvec{o}}{\varvec{g}}{\varvec{N}}{\Delta {\varvec{t}}}^{4})$$ O ( N 2 l o g N Δ t 4 ) . Evaluation against conventional methods on experimental datasets reveals notable improvements: a 41.8% reduction in the minimum number of control nodes, a 36.37% decrease in time of receiving fully control network, and a 38.5% reduction in control algorithm execution time compared to layered model-based control methods. |
| format | Article |
| id | doaj-art-c5ce19c7d2084448a6887d65eaa3a356 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-c5ce19c7d2084448a6887d65eaa3a3562024-11-24T12:38:43ZengSpringerDiscover Applied Sciences3004-92612024-11-0161211410.1007/s42452-024-05883-5A novel controllability method on temporal networks based on tree modelPeyman Arebi0Department of Computer Engineering, Technical and Vocational University (TVU)Abstract 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. In this paper, we embark on a comprehensive exploration of controllability within the realm of temporal networks. A new method for controlling temporal networks is proposed, in which the intervention of all the dynamics of temporal networks can provide the possibility to speed up the network controllability processes. In the proposed method, the network dynamics are stored in the tree data structure to reduce the computational complexity of the algorithm for finding control nodes while maintaining essential information in controllable processes. Results show that the proposed algorithm with linear complexity of $${\varvec{O}}({{\varvec{N}}}^{2}{\varvec{l}}{\varvec{o}}{\varvec{g}}{\varvec{N}}{\Delta {\varvec{t}}}^{4})$$ O ( N 2 l o g N Δ t 4 ) . Evaluation against conventional methods on experimental datasets reveals notable improvements: a 41.8% reduction in the minimum number of control nodes, a 36.37% decrease in time of receiving fully control network, and a 38.5% reduction in control algorithm execution time compared to layered model-based control methods.https://doi.org/10.1007/s42452-024-05883-5Controllability of temporal networksTemporal networksDriver Nodes SetMaximum Flow AlgorithmsTree Model |
| spellingShingle | Peyman Arebi A novel controllability method on temporal networks based on tree model Discover Applied Sciences Controllability of temporal networks Temporal networks Driver Nodes Set Maximum Flow Algorithms Tree Model |
| title | A novel controllability method on temporal networks based on tree model |
| title_full | A novel controllability method on temporal networks based on tree model |
| title_fullStr | A novel controllability method on temporal networks based on tree model |
| title_full_unstemmed | A novel controllability method on temporal networks based on tree model |
| title_short | A novel controllability method on temporal networks based on tree model |
| title_sort | novel controllability method on temporal networks based on tree model |
| topic | Controllability of temporal networks Temporal networks Driver Nodes Set Maximum Flow Algorithms Tree Model |
| url | https://doi.org/10.1007/s42452-024-05883-5 |
| work_keys_str_mv | AT peymanarebi anovelcontrollabilitymethodontemporalnetworksbasedontreemodel AT peymanarebi novelcontrollabilitymethodontemporalnetworksbasedontreemodel |