Detecting the driver nodes of temporal networks
Detecting the driver nodes of complex networks has garnered significant attention recently to control complex systems to desired behaviors, where nodes represent system components and edges encode their interactions. Driver nodes, which are directly controlled by external inputs, play a crucial role...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2023-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/aced66 |
| _version_ | 1797740077087981568 |
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| author | Tingting Qin Gaopeng Duan Aming Li |
| author_facet | Tingting Qin Gaopeng Duan Aming Li |
| author_sort | Tingting Qin |
| collection | DOAJ |
| description | Detecting the driver nodes of complex networks has garnered significant attention recently to control complex systems to desired behaviors, where nodes represent system components and edges encode their interactions. Driver nodes, which are directly controlled by external inputs, play a crucial role in controlling all network nodes. While many approaches have been proposed to identify driver nodes of static networks, we still lack an effective algorithm to control ubiquitous temporal networks, where network structures evolve over time. Here we propose an effective online time-accelerated heuristic algorithm (OTaHa) to detect driver nodes of temporal networks. Together with theoretical analysis and numerical simulations on synthetic and empirical temporal networks, we show that OTaHa offers multiple sets of driver nodes, and noticeably outperforms existing methods in terms of accuracy and execution time. We further report that most edges are redundant in controlling temporal networks although the complete instantaneous signal-carrying edges cannot be guaranteed. Moreover, removing edges with high edge betweenness (the number of all-pairs shortest paths passing through the edge) significantly impedes the overall controllability. Our work provides an effective algorithm and paves the way for subsequent explorations on achieving the ultimate control of temporal networks. |
| first_indexed | 2024-03-12T14:07:20Z |
| format | Article |
| id | doaj.art-0bd8140e524146638b40122b7bb41316 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-12T14:07:20Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-0bd8140e524146638b40122b7bb413162023-08-21T11:50:14ZengIOP PublishingNew Journal of Physics1367-26302023-01-0125808303110.1088/1367-2630/aced66Detecting the driver nodes of temporal networksTingting Qin0https://orcid.org/0009-0003-2634-2869Gaopeng Duan1https://orcid.org/0000-0002-9262-1482Aming Li2https://orcid.org/0000-0003-4045-8721Center for Systems and Control, College of Engineering, Peking University , Beijing 100871, People’s Republic of ChinaCenter for Systems and Control, College of Engineering, Peking University , Beijing 100871, People’s Republic of ChinaCenter for Systems and Control, College of Engineering, Peking University , Beijing 100871, People’s Republic of China; Center for Multi-Agent Research, Institute for Artificial Intelligence, Peking University , Beijing 100871, People’s Republic of ChinaDetecting the driver nodes of complex networks has garnered significant attention recently to control complex systems to desired behaviors, where nodes represent system components and edges encode their interactions. Driver nodes, which are directly controlled by external inputs, play a crucial role in controlling all network nodes. While many approaches have been proposed to identify driver nodes of static networks, we still lack an effective algorithm to control ubiquitous temporal networks, where network structures evolve over time. Here we propose an effective online time-accelerated heuristic algorithm (OTaHa) to detect driver nodes of temporal networks. Together with theoretical analysis and numerical simulations on synthetic and empirical temporal networks, we show that OTaHa offers multiple sets of driver nodes, and noticeably outperforms existing methods in terms of accuracy and execution time. We further report that most edges are redundant in controlling temporal networks although the complete instantaneous signal-carrying edges cannot be guaranteed. Moreover, removing edges with high edge betweenness (the number of all-pairs shortest paths passing through the edge) significantly impedes the overall controllability. Our work provides an effective algorithm and paves the way for subsequent explorations on achieving the ultimate control of temporal networks.https://doi.org/10.1088/1367-2630/aced66complex systemstemporal networksdriver nodes |
| spellingShingle | Tingting Qin Gaopeng Duan Aming Li Detecting the driver nodes of temporal networks New Journal of Physics complex systems temporal networks driver nodes |
| title | Detecting the driver nodes of temporal networks |
| title_full | Detecting the driver nodes of temporal networks |
| title_fullStr | Detecting the driver nodes of temporal networks |
| title_full_unstemmed | Detecting the driver nodes of temporal networks |
| title_short | Detecting the driver nodes of temporal networks |
| title_sort | detecting the driver nodes of temporal networks |
| topic | complex systems temporal networks driver nodes |
| url | https://doi.org/10.1088/1367-2630/aced66 |
| work_keys_str_mv | AT tingtingqin detectingthedrivernodesoftemporalnetworks AT gaopengduan detectingthedrivernodesoftemporalnetworks AT amingli detectingthedrivernodesoftemporalnetworks |