{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:04:36Z","timestamp":1779894276077,"version":"3.53.1"},"reference-count":35,"publisher":"Elsevier BV","issue":"9","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of the Franklin Institute"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.jfranklin.2026.108695","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T05:32:30Z","timestamp":1777527150000},"page":"108695","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["A community-aware hierarchical reinforcement learning framework for resource allocation in service networks"],"prefix":"10.1016","volume":"363","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4965-050X","authenticated-orcid":false,"given":"Jinhong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jfranklin.2026.108695_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2021.102217","article-title":"Service-oriented industrial internet of things gateway for cloud manufacturing","volume":"73","author":"Liu","year":"2022","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"10.1016\/j.jfranklin.2026.108695_bib0002","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.comcom.2020.11.016","article-title":"Industrial Internet of Things and its applications in industry 4.0: state of the art","volume":"166","author":"Malik","year":"2021","journal-title":"Comput. Commun."},{"issue":"3","key":"10.1016\/j.jfranklin.2026.108695_bib0003","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1007\/s10586-021-03316-1","article-title":"Load-balance scheduling for intelligent sensors deployment in industrial internet of things","volume":"25","author":"Sah","year":"2022","journal-title":"Clust. Comput."},{"issue":"5","key":"10.1016\/j.jfranklin.2026.108695_bib0004","first-page":"2146","article-title":"A subgraph-based hierarchical Q-learning approach to optimal resource scheduling for complex industrial networks","volume":"71","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Circuit. Syst. I: Regul. Pap."},{"issue":"6","key":"10.1016\/j.jfranklin.2026.108695_bib0005","doi-asserted-by":"crossref","first-page":"5644","DOI":"10.1109\/TNSE.2022.3141728","article-title":"Edge intelligence-Driven joint offloading and resource allocation for future 6G Industrial Internet of Things","volume":"11","author":"Gong","year":"2024","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"10.1016\/j.jfranklin.2026.108695_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2021.107237","article-title":"Manufacturing service supply-demand optimization with dual diversities for industrial internet platforms","volume":"156","author":"Hao","year":"2021","journal-title":"Comput. Ind. Eng."},{"issue":"4","key":"10.1016\/j.jfranklin.2026.108695_bib0007","doi-asserted-by":"crossref","first-page":"5274","DOI":"10.1109\/TII.2023.3331127","article-title":"Energy-efficient resource allocation for space\u2013air\u2013ground integrated industrial power Internet of Things network","volume":"20","author":"Qin","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"7540","key":"10.1016\/j.jfranklin.2026.108695_bib0008","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"issue":"9","key":"10.1016\/j.jfranklin.2026.108695_bib0009","doi-asserted-by":"crossref","first-page":"16859","DOI":"10.1109\/JIOT.2024.3366009","article-title":"Multidimensional resource management for distributed MEC networks in jamming environment: a hierarchical DRL approach","volume":"11","author":"Liu","year":"2024","journal-title":"IEEE Internet Thing. J."},{"key":"10.1016\/j.jfranklin.2026.108695_bib0010","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.jmsy.2021.08.002","article-title":"Towards self-X cognitive manufacturing network: an industrial knowledge graph-based multi-agent reinforcement learning approach","volume":"61","author":"Zheng","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.jfranklin.2026.108695_bib0011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2016.09.002","article-title":"Community detection in networks: a user guide","volume":"659","author":"Fortunato","year":"2016","journal-title":"Phys. Rep."},{"issue":"1","key":"10.1016\/j.jfranklin.2026.108695_bib0012","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1038\/s41467-022-28379-5","article-title":"Recovery coupling in multilayer networks","volume":"13","author":"Danziger","year":"2022","journal-title":"Nat. Commun."},{"issue":"2","key":"10.1016\/j.jfranklin.2026.108695_bib0013","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/TNSE.2016.2566615","article-title":"Enhancement of synchronizability in networks with community structure through adding efficient inter-community links","volume":"3","author":"Jalili","year":"2016","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"7","key":"10.1016\/j.jfranklin.2026.108695_bib0014","doi-asserted-by":"crossref","first-page":"5031","DOI":"10.1109\/TII.2020.3024170","article-title":"Learning-based resource allocation strategy for industrial IoT in UAV-enabled MEC systems","volume":"17","author":"Sun","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"1","key":"10.1016\/j.jfranklin.2026.108695_bib0015","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TCOMM.2023.3322736","article-title":"Deep reinforcement learning based resource allocation in delay-tolerance-aware 5G industrial IoT systems","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Commun."},{"issue":"12","key":"10.1016\/j.jfranklin.2026.108695_bib0016","doi-asserted-by":"crossref","first-page":"3536","DOI":"10.1109\/JSAC.2024.3459020","article-title":"Traffic-Aware lightweight hierarchical offloading toward adaptive slicing-enabled SAGIN","volume":"42","author":"Chen","year":"2024","journal-title":"IEEE J. Sel. Area. Commun."},{"issue":"12","key":"10.1016\/j.jfranklin.2026.108695_bib0017","doi-asserted-by":"crossref","first-page":"11604","DOI":"10.1109\/TMC.2024.3396511","article-title":"Joint computation offloading and resource allocation in multi-edge smart communities with personalized federated deep reinforcement learning","volume":"23","author":"Chen","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"7","key":"10.1016\/j.jfranklin.2026.108695_bib0018","doi-asserted-by":"crossref","first-page":"6315","DOI":"10.1109\/TMC.2025.3540407","article-title":"Mobility-aware seamless service migration and resource allocation in multi-edge IoV systems","volume":"24","author":"Chen","year":"2025","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"8","key":"10.1016\/j.jfranklin.2026.108695_bib0019","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1109\/TPDS.2021.3132422","article-title":"Adaptive and efficient resource allocation in cloud datacenters using actor-critic deep reinforcement learning","volume":"33","author":"Chen","year":"2022","journal-title":"IEEE Trans. Parall. Distrib. Syst."},{"issue":"1","key":"10.1016\/j.jfranklin.2026.108695_bib0020","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.jfranklin.2026.108695_bib0021","series-title":"Advances in Neural Information Processing Systems","article-title":"Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation","volume":"29","author":"Kulkarni","year":"2016"},{"key":"10.1016\/j.jfranklin.2026.108695_bib0022","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jmsy.2023.06.005","article-title":"Graph neural networks-based scheduler for production planning problems using reinforcement learning","volume":"69","author":"Hameed","year":"2023","journal-title":"J. Manuf. Syst."},{"issue":"2","key":"10.1016\/j.jfranklin.2026.108695_bib0023","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1109\/TII.2022.3189725","article-title":"Flexible job-shop scheduling via graph neural network and deep reinforcement learning","volume":"19","author":"Song","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"12","key":"10.1016\/j.jfranklin.2026.108695_bib0024","doi-asserted-by":"crossref","first-page":"3073","DOI":"10.1109\/TPDS.2023.3313779","article-title":"Task placement and resource allocation for edge machine learning: a gnn-based multi-agent reinforcement learning paradigm","volume":"34","author":"Li","year":"2023","journal-title":"IEEE Trans. Parall. Distrib. Syst."},{"issue":"1","key":"10.1016\/j.jfranklin.2026.108695_bib0025","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/TSG.2023.3267069","article-title":"Physics-informed graphical representation-enabled deep reinforcement learning for robust distribution system voltage control","volume":"15","author":"Cao","year":"2024","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.jfranklin.2026.108695_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2024.114282","article-title":"Application and progress of artificial intelligence technology in the field of distribution network voltage control: a review","volume":"192","author":"Zhang","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"5","key":"10.1016\/j.jfranklin.2026.108695_bib0027","doi-asserted-by":"crossref","first-page":"4848","DOI":"10.1109\/TPWRS.2021.3084469","article-title":"Graph convolutional network-based topology embedded deep reinforcement learning for voltage stability control","volume":"36","author":"Hossain","year":"2021","journal-title":"IEEE Trans. Power Syst."},{"issue":"1","key":"10.1016\/j.jfranklin.2026.108695_bib0028","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/TPWRS.2023.3238377","article-title":"A physics-guided graph convolution neural network for optimal power flow","volume":"39","author":"Gao","year":"2024","journal-title":"IEEE Trans. Power Syst."},{"issue":"5","key":"10.1016\/j.jfranklin.2026.108695_bib0029","doi-asserted-by":"crossref","first-page":"4086","DOI":"10.1109\/TSG.2023.3239740","article-title":"Spatio-temporal graph convolutional neural networks for physics-aware grid learning algorithms","volume":"14","author":"Wu","year":"2023","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.jfranklin.2026.108695_bib0030","series-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","first-page":"2930","article-title":"Sketch-GNN: scalable graph neural networks with sublinear training complexity","volume":"Vol. 35","author":"Ding","year":"2022"},{"key":"10.1016\/j.jfranklin.2026.108695_bib0031","unstructured":"P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio, Graph attention networks, (2017). arXiv preprint arXiv: 1710.10903."},{"key":"10.1016\/j.jfranklin.2026.108695_bib0032","unstructured":"J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, Proximal policy optimization algorithms, (2017). arXiv preprint arXiv: 1707.06347."},{"key":"10.1016\/j.jfranklin.2026.108695_bib0033","series-title":"Proceedings of the 35th International Conference on Machine Learning","first-page":"1587","article-title":"Addressing function approximation error in actor-critic methods","volume":"Vol. 80","author":"Fujimoto","year":"2018"},{"key":"10.1016\/j.jfranklin.2026.108695_bib0034","series-title":"Proceedings of the 31st International Conference on Machine Learning","first-page":"1387","article-title":"Deterministic policy gradient algorithms","volume":"Vol. 32","author":"Silver","year":"2014"},{"key":"10.1016\/j.jfranklin.2026.108695_bib0035","series-title":"Proceedings of the 35th International Conference on Machine Learning","first-page":"1861","article-title":"Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor","volume":"Vol. 80","author":"Haarnoja","year":"2018"}],"container-title":["Journal of the Franklin Institute"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0016003226002954?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0016003226002954?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T14:16:43Z","timestamp":1779891403000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0016003226002954"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":35,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["S0016003226002954"],"URL":"https:\/\/doi.org\/10.1016\/j.jfranklin.2026.108695","relation":{},"ISSN":["0016-0032"],"issn-type":[{"value":"0016-0032","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A community-aware hierarchical reinforcement learning framework for resource allocation in service networks","name":"articletitle","label":"Article Title"},{"value":"Journal of the Franklin Institute","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jfranklin.2026.108695","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc. on behalf of The Franklin Institute.","name":"copyright","label":"Copyright"}],"article-number":"108695"}}