{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T23:48:08Z","timestamp":1783122488506,"version":"3.54.6"},"reference-count":44,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":204,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>The controllability of temporal networks has been one of the most important challenges in this type of network over the last decade. The main goal of network controllability processes is to find the minimum set of control nodes in such a way that all network nodes can be controlled by them. This problem is NP\u2010hard in the temporal networks. In this paper, a controllability method is proposed to improve the efficiency of the controllability process on temporal networks. In the proposed method, a population method based on the ant colony optimization (ACO) algorithm is proposed, which is compatible with temporal networks. Due to the temporal nature of the controllability processes in temporal networks, the ACO algorithm is adapted temporally. Also, due to the time\u2010consuming controllable processes in temporal networks and in order to increase the efficiency of the ACO algorithm, a backpropagation neural network has been used, which finds the minimum driver node set of the network based on the layered model in order to fully control the network nodes. The results of the implementation of the proposed method on real\u2010world datasets demonstrate that the proposed ACO\u2010BPNN method works stably and with high efficiency on high\u2010volume datasets. By comparing the efficiency of the proposed method with conventional controllability methods, it is found that the proposed method has performed better in terms of the speed of execution and the length of the minimum driver node set.<\/jats:p>","DOI":"10.1155\/cplx\/5780747","type":"journal-article","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T04:35:33Z","timestamp":1753418133000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["ACO\u2010Based Neural Network to Enhance the Efficiency of Network Controllability of Temporal Networks"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8839-5629","authenticated-orcid":false,"given":"Jie","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2175-2593","authenticated-orcid":false,"given":"Ling","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4005-3057","authenticated-orcid":false,"given":"Peyman","family":"Arebi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2024.111741"},{"key":"e_1_2_10_2_2","unstructured":"ParkN.-J. KimY.-U. OhK.-H. andAhnH.-S. Controllable Subspaces in Structured Networks of Hierarchical Directed Acyclic Graphs: Controllability of Individual Nodes 2024 https:\/\/arxiv.org\/abs\/2403.01184."},{"key":"e_1_2_10_3_2","volume-title":"Network Integration and Optimization for Biological System Control","author":"Shi T.","year":"2025"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-024-01402-6"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101483"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101517"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118886"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/6695026"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1155\/cplx\/2919169"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/mcas.2023.3236659"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2025.115993"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcss.2024.3459945"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnac054"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/lcsys.2023.3289827"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/aced66"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/16\/12\/123055"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/tits.2023.3269794"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41965-024-00165-w"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/tac.2025.3552001"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/tac.2022.3226701"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-023-00566-3"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/tac.2019.2944381"},{"key":"e_1_2_10_23_2","unstructured":"LiX. Structural Controllability and Optimal Control of Complex Networks 2024."},{"key":"e_1_2_10_24_2","doi-asserted-by":"crossref","unstructured":"JosephG. MoothedathS. andLinJ. Minimal Input Structural Modifications for Strongly Structural Controllability 2024 IEEE 63rd Conference on Decision and Control (CDC) 2024 190\u2013195 https:\/\/doi.org\/10.1109\/cdc56724.2024.10886674.","DOI":"10.1109\/CDC56724.2024.10886674"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnse.2025.3556365"},{"key":"e_1_2_10_26_2","doi-asserted-by":"publisher","DOI":"10.1080\/01969722.2022.2159162"},{"key":"e_1_2_10_27_2","doi-asserted-by":"crossref","unstructured":"OsanlouK. FrankJ. BursucA.et al. Solving Disjunctive Temporal Networks With Uncertainty Under Restricted Time-Based Controllability Using Tree Search and Graph Neural Networks 36 Proceedings of the AAAI Conference on Artificial Intelligence 2022 no. 9 9877\u20139885 https:\/\/doi.org\/10.1609\/aaai.v36i9.21224.","DOI":"10.1609\/aaai.v36i9.21224"},{"key":"e_1_2_10_28_2","doi-asserted-by":"crossref","unstructured":"XieQ.andZhangY. Research on the Interpretability and Controllability of Deep Learning-Based Exhibition Design Style Generation and Transfer 2024 International Conference on Intelligent Computing and Next Generation Networks (ICNGN) 2024 1\u20135.","DOI":"10.1109\/ICNGN63705.2024.10871519"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/tac.2024.3355806"},{"key":"e_1_2_10_30_2","doi-asserted-by":"crossref","unstructured":"PauliP. WangR. ManchesterI. R. andAllg\u00f6werF. Lipschitz-Bounded 1D Convolutional Neural Networks Using the Cayley Transform and the Controllability Gramian 2023 62nd IEEE Conference on Decision and Control (CDC) 2023 5345\u20135350 https:\/\/doi.org\/10.1109\/cdc49753.2023.10383534.","DOI":"10.1109\/CDC49753.2023.10383534"},{"key":"e_1_2_10_31_2","doi-asserted-by":"publisher","DOI":"10.1080\/27690911.2024.2436440"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-05335-3"},{"key":"e_1_2_10_33_2","doi-asserted-by":"crossref","unstructured":"ZhuoM. YangP. ChenJ. LiuL. andLiuC. Adaptive Optimization of Dynamic Heterogeneous Network Topologies: A Simulated Annealing Methodology International Conference on Artificial Intelligence and Security 2022 587\u2013612 https:\/\/doi.org\/10.1007\/978-3-031-06788-4_49.","DOI":"10.1007\/978-3-031-06788-4_49"},{"key":"e_1_2_10_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/tai.2023.3314581"},{"key":"e_1_2_10_35_2","doi-asserted-by":"publisher","DOI":"10.1137\/23m1599744"},{"key":"e_1_2_10_36_2","doi-asserted-by":"publisher","DOI":"10.1063\/5.0071926"},{"key":"e_1_2_10_37_2","doi-asserted-by":"publisher","DOI":"10.31979\/etd.jhc8-t9gj"},{"key":"e_1_2_10_38_2","doi-asserted-by":"publisher","DOI":"10.4171\/aihpc\/151"},{"key":"e_1_2_10_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.118726"},{"key":"e_1_2_10_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2025.116499"},{"key":"e_1_2_10_41_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41596-024-01023-w"},{"key":"e_1_2_10_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-024-4149-x"},{"key":"e_1_2_10_43_2","doi-asserted-by":"crossref","unstructured":"RossiR.andAhmedN. The Network Data Repository With Interactive Graph Analytics and Visualization Proceedings of the AAAI Conference on Artificial Intelligence 2015.","DOI":"10.1609\/aaai.v29i1.9277"},{"key":"e_1_2_10_44_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature10011"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/cplx\/5780747","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/cplx\/5780747","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/cplx\/5780747","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T03:56:05Z","timestamp":1773028565000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/cplx\/5780747"}},"subtitle":[],"editor":[{"given":"Alex","family":"Alexandridis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1155\/cplx\/5780747"],"URL":"https:\/\/doi.org\/10.1155\/cplx\/5780747","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-01-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5780747"}}