{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T20:15:01Z","timestamp":1783196101748,"version":"3.54.6"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376264"],"award-info":[{"award-number":["62376264"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2024MF030"],"award-info":[{"award-number":["ZR2024MF030"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022QF053"],"award-info":[{"award-number":["ZR2022QF053"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014103","name":"Key Technology Research and Development Program of Shandong","doi-asserted-by":"publisher","award":["2025CXPT096"],"award-info":[{"award-number":["2025CXPT096"]}],"id":[{"id":"10.13039\/100014103","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010029","name":"Taishan Scholar Foundation of Shandong Province","doi-asserted-by":"publisher","award":["tsqn202306150"],"award-info":[{"award-number":["tsqn202306150"]}],"id":[{"id":"10.13039\/501100010029","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2022ZD0208700"],"award-info":[{"award-number":["2022ZD0208700"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.knosys.2026.116068","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T15:54:41Z","timestamp":1779292481000},"page":"116068","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["PHANet: Contrastive hypergraph structures and prototype memory for discriminative skeleton-based action recognition"],"prefix":"10.1016","volume":"347","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2082-1162","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Pang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangqi","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunmeng","family":"Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9521-6039","authenticated-orcid":false,"given":"Lei","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.116068_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123143","article-title":"Human activity recognition with smartphone-integrated sensors: A survey","volume":"246","author":"Dentamaro","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116068_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.rcim.2023.102659","article-title":"Skeleton-RGB integrated highly similar human action prediction in human\u2013robot collaborative assembly","volume":"86","author":"Zhang","year":"2024","journal-title":"Robot. Comput.-Integr. Manuf."},{"issue":"8","key":"10.1016\/j.knosys.2026.116068_b3","doi-asserted-by":"crossref","first-page":"5345","DOI":"10.1109\/TPAMI.2024.3367412","article-title":"Cross-modal federated human activity recognition","volume":"46","author":"Yang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116068_b4","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"Defferrard","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116068_b5","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"10.1016\/j.knosys.2026.116068_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125642","article-title":"Vision-based human action quality assessment: A systematic review","volume":"263","author":"Liu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116068_b7","series-title":"2025 IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"9690","article-title":"Autoregressive adaptive hypergraph transformer for skeleton-based activity recognition","author":"Ray","year":"2025"},{"key":"10.1016\/j.knosys.2026.116068_b8","doi-asserted-by":"crossref","first-page":"9388","DOI":"10.52202\/079017-0298","article-title":"Chase: Learning convex hull adaptive shift for skeleton-based multi-entity action recognition","volume":"37","author":"Wen","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116068_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127529","article-title":"Active generation network of human skeleton for action recognition","volume":"281","author":"Wang","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116068_b10","doi-asserted-by":"crossref","unstructured":"P. Zhang, C. Lan, W. Zeng, J. Xing, J. Xue, N. Zheng, Semantics-guided neural networks for efficient skeleton-based human action recognition, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 1112\u20131121.","DOI":"10.1109\/CVPR42600.2020.00119"},{"key":"10.1016\/j.knosys.2026.116068_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127081","article-title":"TSGCNeXt: Dynamic-static multi-graph convolution for efficient skeleton-based action recognition","volume":"276","author":"Liu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116068_b12","series-title":"ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1","article-title":"PASTD: Progressive augmentation and spatiotemporal decoupling contrastive learning for skeleton-based action recognition","author":"Huang","year":"2025"},{"key":"10.1016\/j.knosys.2026.116068_b13","article-title":"Spatial temporal graph convolutional networks for skeleton-based action recognition","volume":"vol. 32","author":"Yan","year":"2018"},{"key":"10.1016\/j.knosys.2026.116068_b14","doi-asserted-by":"crossref","unstructured":"L. Shi, Y. Zhang, J. Cheng, H. Lu, Two-stream adaptive graph convolutional networks for skeleton-based action recognition, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 12026\u201312035.","DOI":"10.1109\/CVPR.2019.01230"},{"key":"10.1016\/j.knosys.2026.116068_b15","doi-asserted-by":"crossref","unstructured":"Y. Chen, Z. Zhang, C. Yuan, B. Li, Y. Deng, W. Hu, Channel-wise topology refinement graph convolution for skeleton-based action recognition, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 13359\u201313368.","DOI":"10.1109\/ICCV48922.2021.01311"},{"issue":"9","key":"10.1016\/j.knosys.2026.116068_b16","doi-asserted-by":"crossref","first-page":"12130","DOI":"10.1109\/TNNLS.2023.3252172","article-title":"Learning heterogeneous spatial\u2013temporal context for skeleton-based action recognition","volume":"35","author":"Gao","year":"2023","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116068_b17","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.neunet.2023.07.051","article-title":"Glimpse and focus: Global and local-scale graph convolution network for skeleton-based action recognition","volume":"167","author":"Gao","year":"2023","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2026.116068_b18","doi-asserted-by":"crossref","unstructured":"K. Shiraki, T. Hirakawa, T. Yamashita, H. Fujiyoshi, Spatial temporal attention graph convolutional networks with mechanics-stream for skeleton-based action recognition, in: Proceedings of the Asian Conference on Computer Vision, 2020.","DOI":"10.1007\/978-3-030-69541-5_21"},{"key":"10.1016\/j.knosys.2026.116068_b19","doi-asserted-by":"crossref","first-page":"9532","DOI":"10.1109\/TIP.2020.3028207","article-title":"Skeleton-based action recognition with multi-stream adaptive graph convolutional networks","volume":"29","author":"Shi","year":"2020","journal-title":"IEEE Trans. Image Process."},{"issue":"12","key":"10.1016\/j.knosys.2026.116068_b20","doi-asserted-by":"crossref","first-page":"8623","DOI":"10.1109\/TCSVT.2022.3194350","article-title":"Motion guided attention learning for self-supervised 3D human action recognition","volume":"32","author":"Yang","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.116068_b21","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"387","article-title":"Dynamic spatial-temporal attention network for skeleton-based action recognition","author":"Shi","year":"2020"},{"key":"10.1016\/j.knosys.2026.116068_b22","series-title":"Hypergcn: Hypergraph convolutional networks for semi-supervised classification","first-page":"1","author":"Yadati","year":"2018"},{"key":"10.1016\/j.knosys.2026.116068_b23","series-title":"2022 IEEE 38th International Conference on Data Engineering","first-page":"1621","article-title":"Dynamic hypergraph convolutional network","author":"Yin","year":"2022"},{"issue":"3","key":"10.1016\/j.knosys.2026.116068_b24","doi-asserted-by":"crossref","first-page":"3181","DOI":"10.1109\/TPAMI.2022.3182052","article-title":"Hgnn+: General hypergraph neural networks","volume":"45","author":"Gao","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116068_b25","doi-asserted-by":"crossref","unstructured":"Y. Zhou, T. Xu, C. Wu, X. Wu, J. Kittler, Adaptive hyper-graph convolution network for skeleton-based human action recognition with virtual connections, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2025, pp. 12648\u201312658.","DOI":"10.1109\/ICCV51701.2025.01175"},{"key":"10.1016\/j.knosys.2026.116068_b26","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1109\/TMM.2021.3127040","article-title":"Efficient spatio-temporal contrastive learning for skeleton-based 3-d action recognition","volume":"25","author":"Gao","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.knosys.2026.116068_b27","series-title":"Graph contrastive learning for skeleton-based action recognition","author":"Huang","year":"2023"},{"key":"10.1016\/j.knosys.2026.116068_b28","first-page":"762","article-title":"Contrastive learning from extremely augmented skeleton sequences for self-supervised action recognition","volume":"vol. 36","author":"Guo","year":"2022"},{"key":"10.1016\/j.knosys.2026.116068_b29","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116068_b30","doi-asserted-by":"crossref","unstructured":"F. Sung, Y. Yang, L. Zhang, T. Xiang, P.H. Torr, T.M. Hospedales, Learning to compare: Relation network for few-shot learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 1199\u20131208.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"10.1016\/j.knosys.2026.116068_b31","article-title":"This looks like that: deep learning for interpretable image recognition","volume":"32","author":"Chen","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116068_b32","doi-asserted-by":"crossref","unstructured":"P. Bateni, R. Goyal, V. Masrani, F. Wood, L. Sigal, Improved few-shot visual classification, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 14493\u201314502.","DOI":"10.1109\/CVPR42600.2020.01450"},{"key":"10.1016\/j.knosys.2026.116068_b33","doi-asserted-by":"crossref","unstructured":"H. Zhou, Q. Liu, Y. Wang, Learning discriminative representations for skeleton based action recognition, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 10608\u201310617.","DOI":"10.1109\/CVPR52729.2023.01022"},{"key":"10.1016\/j.knosys.2026.116068_b34","doi-asserted-by":"crossref","unstructured":"H. Liu, Y. Liu, M. Ren, H. Wang, Y. Wang, Z. Sun, Revealing key details to see differences: A novel prototypical perspective for skeleton-based action recognition, in: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 29248\u201329257.","DOI":"10.1109\/CVPR52734.2025.02723"},{"key":"10.1016\/j.knosys.2026.116068_b35","doi-asserted-by":"crossref","unstructured":"M. Li, S. Chen, X. Chen, Y. Zhang, Y. Wang, Q. Tian, Actional-structural graph convolutional networks for skeleton-based action recognition, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 3595\u20133603.","DOI":"10.1109\/CVPR.2019.00371"},{"key":"10.1016\/j.knosys.2026.116068_b36","doi-asserted-by":"crossref","unstructured":"Z. Liu, H. Zhang, Z. Chen, Z. Wang, W. Ouyang, Disentangling and unifying graph convolutions for skeleton-based action recognition, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 143\u2013152.","DOI":"10.1109\/CVPR42600.2020.00022"},{"key":"10.1016\/j.knosys.2026.116068_b37","series-title":"European Conference on Computer Vision","first-page":"536","article-title":"Decoupling gcn with dropgraph module for skeleton-based action recognition","author":"Cheng","year":"2020"},{"key":"10.1016\/j.knosys.2026.116068_b38","first-page":"1113","article-title":"Multi-scale spatial temporal graph convolutional network for skeleton-based action recognition","volume":"vol. 35","author":"Chen","year":"2021"},{"issue":"2","key":"10.1016\/j.knosys.2026.116068_b39","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1109\/TPAMI.2022.3157033","article-title":"Constructing stronger and faster baselines for skeleton-based action recognition","volume":"45","author":"Song","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116068_b40","doi-asserted-by":"crossref","unstructured":"H.-g. Chi, M.H. Ha, S. Chi, S.W. Lee, Q. Huang, K. Ramani, Infogcn: Representation learning for human skeleton-based action recognition, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20186\u201320196.","DOI":"10.1109\/CVPR52688.2022.01955"},{"key":"10.1016\/j.knosys.2026.116068_b41","doi-asserted-by":"crossref","unstructured":"H. Duan, J. Wang, K. Chen, D. Lin, Pyskl: Towards good practices for skeleton action recognition, in: Proceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 7351\u20137354.","DOI":"10.1145\/3503161.3548546"},{"key":"10.1016\/j.knosys.2026.116068_b42","doi-asserted-by":"crossref","unstructured":"W. Xiang, C. Li, Y. Zhou, B. Wang, L. Zhang, Generative action description prompts for skeleton-based action recognition, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 10276\u201310285.","DOI":"10.1109\/ICCV51070.2023.00943"},{"key":"10.1016\/j.knosys.2026.116068_b43","doi-asserted-by":"crossref","unstructured":"J. Lee, M. Lee, D. Lee, S. Lee, Hierarchically decomposed graph convolutional networks for skeleton-based action recognition, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2023, pp. 10444\u201310453.","DOI":"10.1109\/ICCV51070.2023.00958"},{"key":"10.1016\/j.knosys.2026.116068_b44","first-page":"7579","article-title":"Spatio-temporal fusion for human action recognition via joint trajectory graph","volume":"vol. 38","author":"Zheng","year":"2024"},{"key":"10.1016\/j.knosys.2026.116068_b45","first-page":"6225","article-title":"Dynamic semantic-based spatial graph convolution network for skeleton-based human action recognition","volume":"vol. 38","author":"Xie","year":"2024"},{"key":"10.1016\/j.knosys.2026.116068_b46","doi-asserted-by":"crossref","unstructured":"Y. Zhou, X. Yan, Z.-Q. Cheng, Y. Yan, Q. Dai, X.-S. Hua, Blockgcn: Redefine topology awareness for skeleton-based action recognition, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 2049\u20132058.","DOI":"10.1109\/CVPR52733.2024.00200"},{"key":"10.1016\/j.knosys.2026.116068_b47","doi-asserted-by":"crossref","unstructured":"A. Shahroudy, J. Liu, T.-T. Ng, G. Wang, Ntu rgb+ d: A large scale dataset for 3d human activity analysis, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1010\u20131019.","DOI":"10.1109\/CVPR.2016.115"},{"issue":"10","key":"10.1016\/j.knosys.2026.116068_b48","doi-asserted-by":"crossref","first-page":"2684","DOI":"10.1109\/TPAMI.2019.2916873","article-title":"Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding","volume":"42","author":"Liu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.116068_b49","series-title":"The kinetics human action video dataset","author":"Kay","year":"2017"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512600794X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512600794X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T19:17:13Z","timestamp":1783192633000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095070512600794X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":49,"alternative-id":["S095070512600794X"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116068","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"PHANet: Contrastive hypergraph structures and prototype memory for discriminative skeleton-based action recognition","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116068","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116068"}}